https://wiki.aiisc.ai/index.php?title=Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare&feed=atom&action=historyAugmented Personalized Health: How Smart Data with IoTs and AI is about to Change Healthcare - Revision history2024-03-28T11:26:13ZRevision history for this page on the wikiMediaWiki 1.26.2https://wiki.aiisc.ai/index.php?title=Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare&diff=13210&oldid=prevAdmin: /* kHealth Approach to Personalized Digital Health */2022-10-19T20:38:42Z<p><span dir="auto"><span class="autocomment">kHealth Approach to Personalized Digital Health</span></span></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 20:38, 19 October 2022</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>===Asthma===</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>===Asthma===</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">[[File</del>:<del class="diffchange diffchange-inline">AsthmaRTSI</del>.<del class="diffchange diffchange-inline">PNG|</del>center<del class="diffchange diffchange-inline">|700px|thumb|</del>Fig. 2. The kHealth kit for Asthma Management. The personal level data includes forced exhaled volume in 1 second (FEV1) measured using the spirometer, activity measurement using Fitbit, contextually-relevant health questions using the android application, indoor air quality parameters such as carbon dioxide, volatile organic compounds, indoor temperature and humidity measured using Foobot. The population level real-time IoT data includes pollen level, air quality index, outdoor temperature, and humidity. Finally, public level data includes asthma-related social media tweets. We will utilize the tweets from nearby locations with reference to the patient location to explain anomalous dynamics between personal level, population level, and public level signals.<del class="diffchange diffchange-inline">]]</del></div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline"><html><center><img src="https</ins>:<ins class="diffchange diffchange-inline">//raw.githubusercontent.com/JINU98/khealth/main/2</ins>.<ins class="diffchange diffchange-inline">png"><img></</ins>center<ins class="diffchange diffchange-inline">></html></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Fig. 2. The kHealth kit for Asthma Management. The personal level data includes forced exhaled volume in 1 second (FEV1) measured using the spirometer, activity measurement using Fitbit, contextually-relevant health questions using the android application, indoor air quality parameters such as carbon dioxide, volatile organic compounds, indoor temperature and humidity measured using Foobot. The population level real-time IoT data includes pollen level, air quality index, outdoor temperature, and humidity. Finally, public level data includes asthma-related social media tweets. We will utilize the tweets from nearby locations with reference to the patient location to explain anomalous dynamics between personal level, population level, and public level signals.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Around 6.3 million children in United States suffer from asthma  [10].  Asthma remains  one of  the leading  reasons for pediatric admissions in children's hospitals, and has a prevalence rate of approximately 10% in children, which leads to missed days from school and other societal costs. The kHealth kit for asthma management (Fig. 2) uses low-cost consumer-grade sensor devices such as Foobot, Fitbit, Spirometer, and Android  Tablet  [11].  The  kit provides a platform for continuous monitoring of the patient's personal, public, and population-based health signals and sends alerts to the  patient  and/or  to  the  clinician  based on the patient's condition. These augmentations assist a clinician in determining the precise triggers and the patient susceptibilities, and in deciding the future course of action for prevention and treatment of the disease. More importantly, it can also enable a patient to take better control of their health and well-being by taking more timely  actions  on their own (e.g., in case of asthma, using an inhaler to ward off an asthmatic attack, or remaining indoors to minimize exposure to triggers such as  weed  pollens).  For  instance, we have anecdotal evidence of pediatric patients forced to go to hospital emergency rooms in the wee hours to deal with wheezing attacks and the family incurring significant financial costs that could have been avoided if only we had environmental data and timely alerts.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Around 6.3 million children in United States suffer from asthma  [10].  Asthma remains  one of  the leading  reasons for pediatric admissions in children's hospitals, and has a prevalence rate of approximately 10% in children, which leads to missed days from school and other societal costs. The kHealth kit for asthma management (Fig. 2) uses low-cost consumer-grade sensor devices such as Foobot, Fitbit, Spirometer, and Android  Tablet  [11].  The  kit provides a platform for continuous monitoring of the patient's personal, public, and population-based health signals and sends alerts to the  patient  and/or  to  the  clinician  based on the patient's condition. These augmentations assist a clinician in determining the precise triggers and the patient susceptibilities, and in deciding the future course of action for prevention and treatment of the disease. More importantly, it can also enable a patient to take better control of their health and well-being by taking more timely  actions  on their own (e.g., in case of asthma, using an inhaler to ward off an asthmatic attack, or remaining indoors to minimize exposure to triggers such as  weed  pollens).  For  instance, we have anecdotal evidence of pediatric patients forced to go to hospital emergency rooms in the wee hours to deal with wheezing attacks and the family incurring significant financial costs that could have been avoided if only we had environmental data and timely alerts.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>===Bariatrics===</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>===Bariatrics===</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">[[</del>File:BariatricsRTSI.PNG|center|700px|thumb|Fig. 3. kHealth kit for post-surgery bariatrics patients. The kit contains a water bottle sensor to remind the patient to drink water, a pill bottle sensor for sending reminders to the patient to take their vitamins and minerals, a Fitbit which measures patient’s daily level of activity, and a weighing scale which measures a patient’s body mass index, heart rate, and weight. All these sensors communicates via Bluetooth with an android application that asks relevant questions twice a day.<del class="diffchange diffchange-inline">]]</del></div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline"><html><center><img height="500" src="https://raw.githubusercontent.com/JINU98/khealth/main/3.png"><img></center></html></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>File:BariatricsRTSI.PNG|center|700px|thumb|Fig. 3. kHealth kit for post-surgery bariatrics patients. The kit contains a water bottle sensor to remind the patient to drink water, a pill bottle sensor for sending reminders to the patient to take their vitamins and minerals, a Fitbit which measures patient’s daily level of activity, and a weighing scale which measures a patient’s body mass index, heart rate, and weight. All these sensors communicates via Bluetooth with an android application that asks relevant questions twice a day.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More than 36.5% of US adults were reported clinically obese during 2011-2014 according to a CDC report [13]. The estimated annual medical cost of  obesity  in  the  U.S. was $147 billion US dollars in 2008; and the medical cost for a person who is obese was $1,429 higher than those of normal weight [14]. By 2018 more than 40% of the US adults will be obese and the healthcare cost will rise to $344 billion [15]. It is well established that weight loss surgery can play a significant role in reducing, or even eliminating medical problems associated with obesity. Unfortunately, weight regain is one of the biggest  challenges,  and  more than 50% of patients regain weight within two years or more following their surgery. A lifetime commitment to diet and behavior modifications after surgery is essential for success after undergoing surgery. The main issue in a post-bariatric patient is the lack of follow up years after of their surgery and not checking on their diet plan adherence to other guidance.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>More than 36.5% of US adults were reported clinically obese during 2011-2014 according to a CDC report [13]. The estimated annual medical cost of  obesity  in  the  U.S. was $147 billion US dollars in 2008; and the medical cost for a person who is obese was $1,429 higher than those of normal weight [14]. By 2018 more than 40% of the US adults will be obese and the healthcare cost will rise to $344 billion [15]. It is well established that weight loss surgery can play a significant role in reducing, or even eliminating medical problems associated with obesity. Unfortunately, weight regain is one of the biggest  challenges,  and  more than 50% of patients regain weight within two years or more following their surgery. A lifetime commitment to diet and behavior modifications after surgery is essential for success after undergoing surgery. The main issue in a post-bariatric patient is the lack of follow up years after of their surgery and not checking on their diet plan adherence to other guidance.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>===Pain Management===</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>===Pain Management===</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">[[File</del>:<del class="diffchange diffchange-inline">Pain</del>.<del class="diffchange diffchange-inline">PNG|</del>center<del class="diffchange diffchange-inline">|700px|thumb|</del>Fig. 4. Causal relations between a) hypertension and blood pressure, b) pain and blood pressure, and c) between the three components together.<del class="diffchange diffchange-inline">]]</del></div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline"><html><center><img height="500" src="https</ins>:<ins class="diffchange diffchange-inline">//raw.githubusercontent.com/JINU98/khealth/main/4</ins>.<ins class="diffchange diffchange-inline">png"><img></</ins>center<ins class="diffchange diffchange-inline">></html></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Fig. 4. Causal relations between a) hypertension and blood pressure, b) pain and blood pressure, and c) between the three components together.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Our third example provides insights in the use of AI for augmented  health.  With  age,  pain  is  a  common  problem – studies report a prevalence ranging from 45%-80%, depending on age, the cohort under investigation,  and  the type of residence (independent vs. assisted living) [16]. The physical pain that a patient experiences is both subjective and difficult to quantify, which is what makes the problem both challenging and interesting. It is well known that hypertension causes an increase in blood pressure [17] and that an increase in pain levels (as experienced by patients suffering from chronic healthcare conditions such as sickle cell disease [18]) can cause an increase in their  blood  pressure  levels [19]. However, there are a specific group of individuals who experience hypalgesia and exhibit symptoms of hypertension but do not feel pain as intensely as others  [20]. In order to illustrate the nature of probabilistic modeling, we provide a concrete example of using Bayesian reasoning to model pain and its potential effects using known  medical  knowledge. Fig. 4 indicates the simple one-to-one relationships between hypertension and pain as factors affecting blood pressure.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Our third example provides insights in the use of AI for augmented  health.  With  age,  pain  is  a  common  problem – studies report a prevalence ranging from 45%-80%, depending on age, the cohort under investigation,  and  the type of residence (independent vs. assisted living) [16]. The physical pain that a patient experiences is both subjective and difficult to quantify, which is what makes the problem both challenging and interesting. It is well known that hypertension causes an increase in blood pressure [17] and that an increase in pain levels (as experienced by patients suffering from chronic healthcare conditions such as sickle cell disease [18]) can cause an increase in their  blood  pressure  levels [19]. However, there are a specific group of individuals who experience hypalgesia and exhibit symptoms of hypertension but do not feel pain as intensely as others  [20]. In order to illustrate the nature of probabilistic modeling, we provide a concrete example of using Bayesian reasoning to model pain and its potential effects using known  medical  knowledge. Fig. 4 indicates the simple one-to-one relationships between hypertension and pain as factors affecting blood pressure.</div></td></tr>
</table>Adminhttps://wiki.aiisc.ai/index.php?title=Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare&diff=13209&oldid=prevAdmin: /* Solution Outline: Semantic, Cognitive, and Perceptual Computing */2022-10-19T20:34:53Z<p><span dir="auto"><span class="autocomment">Solution Outline: Semantic, Cognitive, and Perceptual Computing</span></span></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 20:34, 19 October 2022</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l38" >Line 38:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Patient data consists of demographic and medical information from Electronic Medical Records (EMRs) and time series data collected from various environmental sensors, physiological sensors, and public Web resources. This low-level fine-grain data covers various facets ranging from the objective to the contextual and personalized. '''Semantic Computing (SC)''' deals with determining the type and value of the data, and situates it in relationship to other domain concepts. A large body of existing research on ontologies and Semantic Web techniques and technologies can be leveraged for this purpose [8]. '''Cognitive  Computing  (CC)'''  deals with the representation and reasoning related to how humans interpret the data. In the healthcare and medical context, this reflects non-trivial experience and domain expertise exhibited by doctors in  abstracting  and  integrating  multimodal  data to enable actionable insights, taking into account contextual factors such as patient health history, physical characteristics, environmental factors, activity and lifestyle,  to  personalize the future course of action and treatment plans. To mechanize this we need to develop techniques to map raw sensor values to action-related abstractions, taking into account personal details (e.g., high activity translates to different amounts of workout based on age, weight, current health, weather, sport, etc. a low risk of heart problems depends on demographic and ancestry information, food habits, etc.). In general, we need to develop hybrid techniques that combines probabilistic as well as declarative models to formalize normalcy, and thereby detect anomaly. The anomaly itself may later be correlated and explained using patient-volunteered answers to health and  symptoms-related questions.  Anomaly detection is non-trivial because the notion of normalcy itself is intrinsically  dynamic,  based  on  spatio-temporal  context, and requires personalization. It also requires uncovering various correlations among multi-modal data streams and discovering medically-relevant abstract interpretations and the factors that influence them. If sufficient patient data can be obtained through large-scale clinical studies or is personally volunteered, we can also explore the use of deep learning techniques to uncover correlations and abstractions with predictive power.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Patient data consists of demographic and medical information from Electronic Medical Records (EMRs) and time series data collected from various environmental sensors, physiological sensors, and public Web resources. This low-level fine-grain data covers various facets ranging from the objective to the contextual and personalized. '''Semantic Computing (SC)''' deals with determining the type and value of the data, and situates it in relationship to other domain concepts. A large body of existing research on ontologies and Semantic Web techniques and technologies can be leveraged for this purpose [8]. '''Cognitive  Computing  (CC)'''  deals with the representation and reasoning related to how humans interpret the data. In the healthcare and medical context, this reflects non-trivial experience and domain expertise exhibited by doctors in  abstracting  and  integrating  multimodal  data to enable actionable insights, taking into account contextual factors such as patient health history, physical characteristics, environmental factors, activity and lifestyle,  to  personalize the future course of action and treatment plans. To mechanize this we need to develop techniques to map raw sensor values to action-related abstractions, taking into account personal details (e.g., high activity translates to different amounts of workout based on age, weight, current health, weather, sport, etc. a low risk of heart problems depends on demographic and ancestry information, food habits, etc.). In general, we need to develop hybrid techniques that combines probabilistic as well as declarative models to formalize normalcy, and thereby detect anomaly. The anomaly itself may later be correlated and explained using patient-volunteered answers to health and  symptoms-related questions.  Anomaly detection is non-trivial because the notion of normalcy itself is intrinsically  dynamic,  based  on  spatio-temporal  context, and requires personalization. It also requires uncovering various correlations among multi-modal data streams and discovering medically-relevant abstract interpretations and the factors that influence them. If sufficient patient data can be obtained through large-scale clinical studies or is personally volunteered, we can also explore the use of deep learning techniques to uncover correlations and abstractions with predictive power.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">[[File</del>:<del class="diffchange diffchange-inline">ScCcPcRTSI</del>.<del class="diffchange diffchange-inline">PNG|</del>center<del class="diffchange diffchange-inline">|700px|thumb|</del>Fig. 1. Semantic, Cognitive, and Perceptual computing in an asthma-management scenario. The Semantic Cognitive (SC) system gives meaning to the raw data. The Cognitive Computing (CC) system gleans statistics and abstractions from patient observations and asthma literature to enable human interpretation, and the Perceptual Computing (PC) system iteratively interprets and explores them to learn personalized and context-specific normalcy for the patient and from it anomalies. REM: Rapid eye movement  [7]).<del class="diffchange diffchange-inline">]]</del></div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline"><html><center><img src="https</ins>:<ins class="diffchange diffchange-inline">//raw.githubusercontent.com/JINU98/khealth/main/1</ins>.<ins class="diffchange diffchange-inline">png"><img></</ins>center<ins class="diffchange diffchange-inline">></html></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Fig. 1. Semantic, Cognitive, and Perceptual computing in an asthma-management scenario. The Semantic Cognitive (SC) system gives meaning to the raw data. The Cognitive Computing (CC) system gleans statistics and abstractions from patient observations and asthma literature to enable human interpretation, and the Perceptual Computing (PC) system iteratively interprets and explores them to learn personalized and context-specific normalcy for the patient and from it anomalies. REM: Rapid eye movement  [7]).</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Perceptual Computing (PC)''',  in  its  simplest  incarnation, is founded on rich domain knowledge that connects causes with effects and on reasoning strategies that can predict the effects of causes and explain the effects using causes. In a more general setting, perceptual cycle refers to interpreting current sensed data, attempting to build a model of the current situation, determining incomplete or ambiguous information and automatically seeking additional data in a targeted fashion, to minimize uncertainty. This knowledge can take several forms, and learning for creating relevant knowledge applicable to a range of data abstractions, from fine-grained data to coarse-level groupings, is challenging. The knowledge can be deterministic or probabilistic, transcending abstraction levels. In general, the symptoms manifested by a patient are a function of patient characteristics, how vulnerable/susceptible a patient is, what preventive measures/medications/treatments the patient takes, and how intense are the triggers. A key open problem is how to synthesize the vulnerability score associated with  a patient  with respect  to  a relevant  health management objective to better capture the influences of aforementioned issues, and a control level to quantify and express the effectiveness of remedial measures in a manner that is readily accessible to end users (whether a patient or clinician). Orthogonal to these issues is the development of efficient and effective strategies to perform requisite perceptual computations (such as the interleaved use of abductive and deductive reasoning steps) on a resource-constrained mobile platform that hosts all the sensors and to query necessary sensors for additional data needed to synthesize actionable information and alerts [9]. This is very challenging due to the multi-factorial nature of chronic  diseases and usability issues for lay end users (patients). The Perceptual computing builds on Semantic and Cognitive computing to recommend timely and highly personalized actions understandable to humans (patient and clinician) which can lead to improved health outcomes.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Perceptual Computing (PC)''',  in  its  simplest  incarnation, is founded on rich domain knowledge that connects causes with effects and on reasoning strategies that can predict the effects of causes and explain the effects using causes. In a more general setting, perceptual cycle refers to interpreting current sensed data, attempting to build a model of the current situation, determining incomplete or ambiguous information and automatically seeking additional data in a targeted fashion, to minimize uncertainty. This knowledge can take several forms, and learning for creating relevant knowledge applicable to a range of data abstractions, from fine-grained data to coarse-level groupings, is challenging. The knowledge can be deterministic or probabilistic, transcending abstraction levels. In general, the symptoms manifested by a patient are a function of patient characteristics, how vulnerable/susceptible a patient is, what preventive measures/medications/treatments the patient takes, and how intense are the triggers. A key open problem is how to synthesize the vulnerability score associated with  a patient  with respect  to  a relevant  health management objective to better capture the influences of aforementioned issues, and a control level to quantify and express the effectiveness of remedial measures in a manner that is readily accessible to end users (whether a patient or clinician). Orthogonal to these issues is the development of efficient and effective strategies to perform requisite perceptual computations (such as the interleaved use of abductive and deductive reasoning steps) on a resource-constrained mobile platform that hosts all the sensors and to query necessary sensors for additional data needed to synthesize actionable information and alerts [9]. This is very challenging due to the multi-factorial nature of chronic  diseases and usability issues for lay end users (patients). The Perceptual computing builds on Semantic and Cognitive computing to recommend timely and highly personalized actions understandable to humans (patient and clinician) which can lead to improved health outcomes.</div></td></tr>
</table>Adminhttps://wiki.aiisc.ai/index.php?title=Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare&diff=12730&oldid=prevAdmin: /* Also Related */2021-05-13T17:44:11Z<p><span dir="auto"><span class="autocomment">Also Related</span></span></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 17:44, 13 May 2021</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>[[KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care|kHealth-Asthma: Semantic Multisensory Mobile Approach to Personalized Asthma Care]].</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>[[KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care|kHealth-Asthma: Semantic Multisensory Mobile Approach to Personalized Asthma Care]].</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[http://wiki.<del class="diffchange diffchange-inline">knoesis</del>.<del class="diffchange diffchange-inline">org</del>/index.php/Bariatrics  kHealth for Bariatric]</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[http://wiki.<ins class="diffchange diffchange-inline">aiisc</ins>.<ins class="diffchange diffchange-inline">ai</ins>/index.php/Bariatrics  kHealth for Bariatric]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>kHealth Vision:  </div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>kHealth Vision:  </div></td></tr>
</table>Adminhttps://wiki.aiisc.ai/index.php?title=Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare&diff=12720&oldid=prevUtkarshani: /* Also Related */2021-02-18T09:20:56Z<p><span dir="auto"><span class="autocomment">Also Related</span></span></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 09:20, 18 February 2021</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{#ev:youtube|https://www.youtube.com/watch?v=mATRAQ90wio&feature=youtu.be|600|center}}</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{#ev:youtube|https://www.youtube.com/watch?v=mATRAQ90wio&feature=youtu.be|600|center}}</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[https://www.linkedin.com/pulse/augmenting-health-personalized-data-ai-amit-sheth/ TEDx Talk: Augmenting health with personalized data and AI]:</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">{{#ev:youtube|https://www.youtube.com/watch?v=fugE7_y3QWY&feature=youtu.be|600|center}}</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Publications==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Publications==</div></td></tr>
</table>Utkarshanihttps://wiki.aiisc.ai/index.php?title=Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare&diff=11569&oldid=prevDipesh: /* Publications */2018-06-09T04:48:07Z<p><span dir="auto"><span class="autocomment">Publications</span></span></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Publications==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Publications==</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>#Amit Sheth, Yip <del class="diffchange diffchange-inline">HYung</del>, Utkarshani Jaimini, Kadariya D, Vaikunth Sridharan, Venkataramanan R, Tanvi Banerjee, Thirunarayam K, Maninder Kalra. [http://knoesis.org/node/2902 Augmented Personalized Health: Using Semantically Integrated Multimodal Data for Patient Empowered Health Management Strategies]. mHealth Technology Showcase, National Institute of Health- June 2018.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>#Amit Sheth, <ins class="diffchange diffchange-inline">Hong Yung </ins>Yip, Utkarshani Jaimini, Kadariya D, Vaikunth Sridharan, Venkataramanan R, Tanvi Banerjee, Thirunarayam K, Maninder Kalra. [http://knoesis.org/node/2902 Augmented Personalized Health: Using Semantically Integrated Multimodal Data for Patient Empowered Health Management Strategies]. mHealth Technology Showcase, National Institute of Health- June 2018.</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>#Amit Sheth, Utkarshani Jaimini, Yip <del class="diffchange diffchange-inline">HYung</del>. [http://knoesis.org/node/2893 How Will the Internet of Things Enable Augmented Personalized Health?]. IEEE Intelligent Systems. IEEE; 2018 ;33(1).</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>#Amit Sheth, Utkarshani Jaimini, <ins class="diffchange diffchange-inline">Hong Yung </ins>Yip. [http://knoesis.org/node/2893 How Will the Internet of Things Enable Augmented Personalized Health?]. IEEE Intelligent Systems. IEEE; 2018 ;33(1).</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div></div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==References==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==References==</div></td></tr>
</table>Dipeshhttps://wiki.aiisc.ai/index.php?title=Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare&diff=11563&oldid=prevUtkarshani at 17:29, 8 June 20182018-06-08T17:29:27Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{#ev:youtube|https://www.youtube.com/watch?v=mATRAQ90wio&feature=youtu.be|600|center}}</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{#ev:youtube|https://www.youtube.com/watch?v=mATRAQ90wio&feature=youtu.be|600|center}}</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==Publications==</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">#Amit Sheth, Yip HYung, Utkarshani Jaimini, Kadariya D, Vaikunth Sridharan, Venkataramanan R, Tanvi Banerjee, Thirunarayam K, Maninder Kalra. [http://knoesis.org/node/2902 Augmented Personalized Health: Using Semantically Integrated Multimodal Data for Patient Empowered Health Management Strategies]. mHealth Technology Showcase, National Institute of Health- June 2018.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">#Amit Sheth, Utkarshani Jaimini, Yip HYung. [http://knoesis.org/node/2893 How Will the Internet of Things Enable Augmented Personalized Health?]. IEEE Intelligent Systems. IEEE; 2018 ;33(1).</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==References==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==References==</div></td></tr>
</table>Utkarshanihttps://wiki.aiisc.ai/index.php?title=Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare&diff=11541&oldid=prevUtkarshani: /* Also Related */2018-05-20T02:36:41Z<p><span dir="auto"><span class="autocomment">Also Related</span></span></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Also Related==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Also Related==</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>A follow-on to the above article: [https://<del class="diffchange diffchange-inline">arxiv</del>.org/<del class="diffchange diffchange-inline">abs</del>/<del class="diffchange diffchange-inline">1801.00356 </del> How Will The Internet Of Things Enable Augmented Personalized Health?] in IEEE Intelligent Systems, 33 (1), Jan-Feb 2018.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>A follow-on to the above article: [https://<ins class="diffchange diffchange-inline">ieeexplore.ieee</ins>.org/<ins class="diffchange diffchange-inline">document/8355891</ins>/  How Will The Internet Of Things Enable Augmented Personalized Health?] in IEEE Intelligent Systems, 33 (1), Jan-Feb 2018.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>[[KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care|kHealth-Asthma: Semantic Multisensory Mobile Approach to Personalized Asthma Care]].</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>[[KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care|kHealth-Asthma: Semantic Multisensory Mobile Approach to Personalized Asthma Care]].</div></td></tr>
</table>Utkarshanihttps://wiki.aiisc.ai/index.php?title=Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare&diff=11540&oldid=prevUtkarshani: /* References */2018-05-20T02:35:21Z<p><span dir="auto"><span class="autocomment">References</span></span></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>#(2017, May) Stethoscope. [Online]. Available: https://en.wikipedia.org/wiki/Stethoscope</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>#(2017, May) Stethoscope. [Online]. Available: https://en.wikipedia.org/wiki/Stethoscope</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>#A. Sheth and P. Anantharam, “Physical cyber social computing for human experience,” pp. 1:1–1:7, 2013. [Online]. Available: http://doi.acm.org/10.1145/2479787.2479865</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>#A. Sheth and P. Anantharam, “Physical cyber social computing for human experience,” pp. 1:1–1:7, 2013. [Online]. Available: http://doi.acm.org/10.1145/2479787.2479865</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>#A. Sheth, “Smart datahow you and i will exploit big data for personalized digital health and many other activities,” pp. 2–3, 2014.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>#A. Sheth, “Smart datahow you and i will exploit big data for personalized digital health and many other activities,” pp. 2–3, 2014. <ins class="diffchange diffchange-inline">Available: https://ieeexplore.ieee.org/document/7004204/</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>#(2017, Apr) Is watson the best medicine? how big data analysis impacts healthcare. [Online]. Available: https://www.ibm.com/blogs/internet-of-things/iot-and-healthcare/</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>#(2017, Apr) Is watson the best medicine? how big data analysis impacts healthcare. [Online]. Available: https://www.ibm.com/blogs/internet-of-things/iot-and-healthcare/</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>#T. Banerjee and A. Sheth, “Iot quality control for data and application needs,” IEEE Intelligent Systems, vol. 32, no. 2, pp. 68–73, 2017.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>#T. Banerjee and A. Sheth, “Iot quality control for data and application needs,” IEEE Intelligent Systems, vol. 32, no. 2, pp. 68–73, 2017.</div></td></tr>
</table>Utkarshanihttps://wiki.aiisc.ai/index.php?title=Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare&diff=11025&oldid=prevUtkarshani: /* Introduction */2018-02-08T14:43:34Z<p><span dir="auto"><span class="autocomment">Introduction</span></span></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{#ev:youtube|https://www.youtube.com/watch?v=wDi1mLLyxuc&feature=youtu.be|500|center}}</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{#ev:youtube|https://www.youtube.com/watch?v=wDi1mLLyxuc&feature=youtu.be|500|center}}</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Augmented Personalized Health: using AI techniques on semantically integrated multimodal data for patient empowered health management strategies, </ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Keynote at 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA </ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
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</table>Utkarshanihttps://wiki.aiisc.ai/index.php?title=Augmented_Personalized_Health:_How_Smart_Data_with_IoTs_and_AI_is_about_to_Change_Healthcare&diff=11023&oldid=prevUtkarshani: /* Introduction */2018-02-08T06:13:20Z<p><span dir="auto"><span class="autocomment">Introduction</span></span></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 06:13, 8 February 2018</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Introduction==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Introduction==</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The invention of stethoscope in 1816 by Rene Laennec fundamentally changed healthcare [1]. The earliest prototype consisted of a monaural wooden tube that for the very first time, allowed clinicians to investigate a patient's physiology without simply relying on what the patient self-reported. This marked the beginning of data-driven clinical diagnosis that fundamentally changed healthcare. A similar gestalt shift is happening now with the advent of low-cost sensors, wearables, mobile computing, and AI. The days of episodic healthcare where a clinician relies on information collected during a patient visit or <del class="diffchange diffchange-inline"> </del>what  is  reported  in  the  lab  tests he orders is coming to an end. We now have the ability to continuously monitor a patient not only in the clinical setting but in their homes, capturing physiological and environmental data across personal, public, and population levels.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The invention of stethoscope in 1816 by Rene Laennec fundamentally changed healthcare [1]. The earliest prototype consisted of a monaural wooden tube that for the very first time, allowed clinicians to investigate a patient's physiology without simply relying on what the patient self-reported. This marked the beginning of data-driven clinical diagnosis that fundamentally changed healthcare. A similar gestalt shift is happening now with the advent of low-cost sensors, wearables, mobile computing, and AI. The days of episodic healthcare where a clinician relies on information collected during a patient visit or what  is  reported  in  the  lab  tests he orders is coming to an end. We now have the ability to continuously monitor a patient not only in the clinical setting but in their homes, capturing physiological and environmental data across personal, public, and population levels.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Augmented Personalized Healthcare (APH)''' is expected to enhance healthcare by personalizing the use of all relevant physical, cyber, and social data [2] obtained from wearables, sensors and Internet of Things (IoTs), mobile applications, Electronic Medical Records (EMRs), web-based information, and social media. The exploitation of all relevant data, relevant medical knowledge, and AI techniques will extend and enhance human health and well-being. The concept of augmentation  refers  to  the  aggregation  and  integration  of all the signals at the personal, public and population level obtained by analyzing data and knowledge from sensors and the Web that can affect human health, and then converting these signals and data into actions that improve health- related outcomes. These signals collected both passively (without patient engagement)  and  actively  (with  patient or physician engagement) can help make better and more timely decisions. This embodiment of APH  is  an  entirely new approach to human health compared to the current episodic system of periodic care primarily centered around healthcare establishments (such as clinics, hospitals, and labs). APH involves continuous monitoring, engagement, and health management, where rather than treating a patient for a disease, the focus shifts to involving the patient in preventing disease, predicting possible adverse outcomes and preventing them through proactive measures, and keeping them healthy and fit with lifestyle changes. Rather than chronic disease management, it takes a holistic approach to improving the overall quality of life.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>'''Augmented Personalized Healthcare (APH)''' is expected to enhance healthcare by personalizing the use of all relevant physical, cyber, and social data [2] obtained from wearables, sensors and Internet of Things (IoTs), mobile applications, Electronic Medical Records (EMRs), web-based information, and social media. The exploitation of all relevant data, relevant medical knowledge, and AI techniques will extend and enhance human health and well-being. The concept of augmentation  refers  to  the  aggregation  and  integration  of all the signals at the personal, public and population level obtained by analyzing data and knowledge from sensors and the Web that can affect human health, and then converting these signals and data into actions that improve health- related outcomes. These signals collected both passively (without patient engagement)  and  actively  (with  patient or physician engagement) can help make better and more timely decisions. This embodiment of APH  is  an  entirely new approach to human health compared to the current episodic system of periodic care primarily centered around healthcare establishments (such as clinics, hospitals, and labs). APH involves continuous monitoring, engagement, and health management, where rather than treating a patient for a disease, the focus shifts to involving the patient in preventing disease, predicting possible adverse outcomes and preventing them through proactive measures, and keeping them healthy and fit with lifestyle changes. Rather than chronic disease management, it takes a holistic approach to improving the overall quality of life.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{#ev:youtube|https://www.youtube.com/watch?v=wDi1mLLyxuc&feature=youtu.be|500|center}}</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{#ev:youtube|https://www.youtube.com/watch?v=wDi1mLLyxuc&feature=youtu.be|500|center}}</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><center></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">{{#widget:SlideShare|id=86941305&doc=aph-healthintelligence-180130231454|width=500|border=0}}</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></center></ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Challenges==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Challenges==</div></td></tr>
</table>Utkarshani