KHealth Chatbots - Revision history

Dipesh: /* Chatbot for Nutrition */

2020-03-22T23:23:21Z

‎Chatbot for Nutrition

Dipesh: /* Chatbot for Depression */

2020-03-22T23:23:10Z

‎Chatbot for Depression

Dipesh: /* Chatbot for Asthma management */

2020-03-22T23:22:57Z

‎Chatbot for Asthma management

Revathyv: /* Chatbot for depression */

2019-11-03T02:03:49Z

‎Chatbot for depression

Dipesh: /* Publications */

2019-08-15T15:32:43Z

‎Publications

Revathyv: /* Motivation and Background */

2019-08-12T23:04:04Z

‎Motivation and Background

Revathyv: /* Citation */

2019-08-12T22:01:50Z

‎Citation

Revathyv at 22:00, 12 August 2019

2019-08-12T22:00:50Z

Revathyv at 19:05, 12 August 2019

2019-08-12T19:05:13Z

Revathyv at 16:17, 12 August 2019

2019-08-12T16:17:23Z

@@ Line 37: / Line 37: @@
 Obesity is a common yet disastrous lifestyle disease worldwide. It is one of the leading cause of the overall global burden of disease. The number of calories contained in food is not always intuitive to humans and are responsible for the increased prevalence of obesity. There is a need to prevent overweight transitioning from obesity and minimizing obesity-induced hospitalization. While there exists several mobile and web applications that attempt to bridge this gap, they are either not user-friendly and ubiquitous for the day-to-day use by the general public. Increased adoption and ubiquity of mobile health (mHealth) technology such as conversational systems (chatbot) can be proposed to assist in such situations. Intelligent user-chatbot interactions are speculated to have an empowering effect for users to maintain their healthy lifestyle. We developed NOURICH, an interdisciplinary project that integrates food knowledge and their nutritional values to augment diet planning for personalized health. It is first of a kind voice assistant and its novelty includes leveraging two knowledge graphs (domain-specific and personalized user-centric) to (i) provide long-term tracking of food log, diet adherence, and daily calorie intake in a ubiquitous manner and (ii) identify diet habits of the users through images and incorporate them to profile the users to deliver personalized and effective dieting guidelines. We also discussed on how this user-centric voice assistant can be used to augment personalized healthcare for (i) self-monitoring, (ii) self-appraisal, (iii) self-management, (iv) intervention, and (v) disease progression tracking and tracking.
-<b> For detailed information visit - [http://wiki.knoesis.org/index.php/Nourich Nourich wiki page] </b>
+<b> For detailed information visit - [http://wiki.aiisc.ai/index.php/Nourich Nourich wiki page] </b>
 *'''Members'''

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 #Detects potential ADRs of antidepressant drugs
-<b> For detailed information visit - [http://wiki.knoesis.org/index.php/ReaCTrack ReaCTrack wiki page] </b>
+<b> For detailed information visit - [http://wiki.aiisc.ai/index.php/ReaCTrack ReaCTrack wiki page] </b>
 *'''Members'''

@@ Line 5: / Line 5: @@
 kBot is a knowledge-driven personalized chatbot system designed to continuously track medication adherence of pediatric asthmatic patients (age 8 to 15) and monitor relevant health and environmental data. The outcome is to help asthma patients self manage their asthma progression by generating trigger alerts and educate them with various self-management strategies. kBot takes the form of an Android application with a frontend chat interface capable of conversing both text and voice-based conversations and a backend cloud-based server application that handles data collection, processing, and dialogue management. The domain knowledge component is pieced together from the Asthma and Allergy Foundation of America, Mayoclinic, and Verywell Health as well as our clinical collaborator. Whereas, the personalization aspect is derived from the patient’s history of asthma collected from the questionnaires and day-to-day conversations. The system has been evaluated by eight asthma clinicians and eight computer science researchers for chatbot quality, technology acceptance, and system usability. kBOT achieved an overall technology acceptance score of greater than 8 on an 11-point Likert scale and a mean System Usability Score (SUS) greater than 80 from both evaluation groups.t for Asthma
-<b> For detailed information visit - [http://wiki.knoesis.org/index.php/KBot kBot wiki page] </b>
+<b> For detailed information visit - [http://wiki.aiisc.ai/index.php/KBot kBot wiki page] </b>
 *'''Members'''
@@ Line 14: / Line 14: @@
 *'''Contact'''
 **[https://www.linkedin.com/in/dipesh-kadariya-451b4413b/ Dipesh Kadariya]
 ==Chatbot for Depression==

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 **[https://www.linkedin.com/in/dipesh-kadariya-451b4413b/ Dipesh Kadariya]
-==Chatbot for depression==
+==Chatbot for Depression==
 ReaCTrack is the acronym for Personalized Adverse Reaction Conversational-based Tracker for Clinical Depression. This is an interdisciplinary project of applying conversational Artificial Intelligence (AI) for personalized healthcare. The objective of ReaCTrack is to monitor and keep track of personal well-being and depressive symptoms of patients diagnosed with mental health disorders, with the overall goal of delivering personalized and efficient behavioral or medical interventions. It is a spin-off project of Depression.Through different analysis using machine learning (ML) and natural language processing (NLP), ReaCTrack extracts relevant information that is crucial to address the following issues:

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 ==Publications==
-# Dipesh Kadariya, Revathy Venkataramanan, Hong Yung Yip, Maninder Kalra, Thirunarayan Krishnaprasad, Amit Sheth. "kBot: Knowledge-enabled Personalized Chatbot for Asthma Self-Management". In Proceedings of the IEEE SMARTSYS Workshop on Smart Service Systems (SMARTCOMP 2019). IEEE, 2019.
+# Dipesh Kadariya, Revathy Venkataramanan, Hong Yung Yip, Maninder Kalra, Krishnaprasad Thirunarayan, Amit Sheth. "kBot: Knowledge-enabled Personalized Chatbot for Asthma Self-Management". In Proceedings of the IEEE SMARTSYS Workshop on Smart Service Systems (SMARTCOMP 2019). IEEE, 2019.
 # Amit Sheth, Hong Yung Yip, Arun Iyengar, Paul Tepper. [https://ieeexplore.ieee.org/abstract/document/8700320 Cognitive Services and Intelligent Chatbots: Current Perspectives and Special Issue Introduction].  IEEE Internet Computing, 23 (2), March-April 2019.

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 ==Motivation and Background==
-A chatbot (also known as a conversational agent or virtual assistant) on the other spectrum, has also became increasingly popular due to its capability of simulating human-like conversations with a user through speech, text and multimodal communication (http://bit.ly/Chatbot-Media). In fact, an interesting trend is that chatbot-assisted queries are 200 times more conversational than search, and users are demanding more human-style interaction (http://bit.ly/Chatbot-Trend). However, most of today's chatbots do not truly understand natural language, nor do they have cognitive capabilities to understand the context of their conversations, like world knowledge or commonsense reasoning. This means that they lack the ability to go beyond scripted conversations to make interactions with computers feel like natural conversations. Rapid progress in conversational AI facilitated by continuing advances in machine learning (ML) and natural language processing (NLP), and other cognitive services may usher in the next generation of these systems, enabling them to move beyond simple, scripted conversations and towards richer conversational interaction. What started as speech focused chatbots are rapidly being integrated with smart displays that are sure to add visual component to the next generation of chatbots.
+The chatbot (also known as a conversational agent or virtual assistant) has became increasingly popular due to its capability of simulating human-like conversations with a user through speech, text and multimodal communication (http://bit.ly/Chatbot-Media). In fact, an interesting trend is that chatbot-assisted queries are 200 times more conversational than search, and users are demanding more human-style interaction (http://bit.ly/Chatbot-Trend). However, most of today's chatbots do not truly understand natural language, nor do they have cognitive capabilities to understand the context of their conversations, like world knowledge or commonsense reasoning. This means that they lack the ability to go beyond scripted conversations to make interactions with computers feel like natural conversations. Rapid progress in conversational AI facilitated by continuing advances in machine learning (ML) and natural language processing (NLP), and other cognitive services may usher in the next generation of these systems, enabling them to move beyond simple, scripted conversations and towards richer conversational interaction. What started as speech focused chatbots are rapidly being integrated with smart displays that are sure to add visual component to the next generation of chatbots.
 ==Chatbot for Asthma management==

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 ===Citation===
-A. Sheth, Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Semantic-Cognitive-Perceptual Computing for Augmented Personalized Health, The 2nd AAAI Workshop on Reasoning and Learning for Human-Machine Dialogues: DEEP-DIAL 2019 @ AAAI-19, Jan 27, 2019, Hawaii, USA
+A. Sheth, [https://www.slideshare.net/apsheth/towards-smart-chatbots-for-enhanced-health-using-multisensory-sensing-semanticcognitiveperceptual-computing-for-augmented-personalized-health Towards Smart Chatbots for Enhanced Health: Using Multisensory Sensing & Semantic-Cognitive-Perceptual Computing for Augmented Personalized Health], The 2nd AAAI Workshop on Reasoning and Learning for Human-Machine Dialogues: DEEP-DIAL 2019 @ AAAI-19, Jan 27, 2019, Hawaii, USA
 ==A brief video of kHealth Chatbots==

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 ==A brief video of kHealth Chatbots==

← Older revision		Revision as of 19:05, 12 August 2019
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		+	==Motivation and Background==
		+	A chatbot (also known as a conversational agent or virtual assistant) on the other spectrum, has also became increasingly popular due to its capability of simulating human-like conversations with a user through speech, text and multimodal communication (http://bit.ly/Chatbot-Media). In fact, an interesting trend is that chatbot-assisted queries are 200 times more conversational than search, and users are demanding more human-style interaction (http://bit.ly/Chatbot-Trend). However, most of today's chatbots do not truly understand natural language, nor do they have cognitive capabilities to understand the context of their conversations, like world knowledge or commonsense reasoning. This means that they lack the ability to go beyond scripted conversations to make interactions with computers feel like natural conversations. Rapid progress in conversational AI facilitated by continuing advances in machine learning (ML) and natural language processing (NLP), and other cognitive services may usher in the next generation of these systems, enabling them to move beyond simple, scripted conversations and towards richer conversational interaction. What started as speech focused chatbots are rapidly being integrated with smart displays that are sure to add visual component to the next generation of chatbots.
		+
	==Chatbot for Asthma management==		==Chatbot for Asthma management==
	kBot is a knowledge-driven personalized chatbot system designed to continuously track medication adherence of pediatric asthmatic patients (age 8 to 15) and monitor relevant health and environmental data. The outcome is to help asthma patients self manage their asthma progression by generating trigger alerts and educate them with various self-management strategies. kBot takes the form of an Android application with a frontend chat interface capable of conversing both text and voice-based conversations and a backend cloud-based server application that handles data collection, processing, and dialogue management. The domain knowledge component is pieced together from the Asthma and Allergy Foundation of America, Mayoclinic, and Verywell Health as well as our clinical collaborator. Whereas, the personalization aspect is derived from the patient’s history of asthma collected from the questionnaires and day-to-day conversations. The system has been evaluated by eight asthma clinicians and eight computer science researchers for chatbot quality, technology acceptance, and system usability. kBOT achieved an overall technology acceptance score of greater than 8 on an 11-point Likert scale and a mean System Usability Score (SUS) greater than 80 from both evaluation groups.t for Asthma		kBot is a knowledge-driven personalized chatbot system designed to continuously track medication adherence of pediatric asthmatic patients (age 8 to 15) and monitor relevant health and environmental data. The outcome is to help asthma patients self manage their asthma progression by generating trigger alerts and educate them with various self-management strategies. kBot takes the form of an Android application with a frontend chat interface capable of conversing both text and voice-based conversations and a backend cloud-based server application that handles data collection, processing, and dialogue management. The domain knowledge component is pieced together from the Asthma and Allergy Foundation of America, Mayoclinic, and Verywell Health as well as our clinical collaborator. Whereas, the personalization aspect is derived from the patient’s history of asthma collected from the questionnaires and day-to-day conversations. The system has been evaluated by eight asthma clinicians and eight computer science researchers for chatbot quality, technology acceptance, and system usability. kBOT achieved an overall technology acceptance score of greater than 8 on an 11-point Likert scale and a mean System Usability Score (SUS) greater than 80 from both evaluation groups.t for Asthma

← Older revision		Revision as of 16:17, 12 August 2019
Line 1:		Line 1:
−	~~==Motivation and Background==~~
−
−
	==Chatbot for Asthma management==		==Chatbot for Asthma management==
	kBot is a knowledge-driven personalized chatbot system designed to continuously track medication adherence of pediatric asthmatic patients (age 8 to 15) and monitor relevant health and environmental data. The outcome is to help asthma patients self manage their asthma progression by generating trigger alerts and educate them with various self-management strategies. kBot takes the form of an Android application with a frontend chat interface capable of conversing both text and voice-based conversations and a backend cloud-based server application that handles data collection, processing, and dialogue management. The domain knowledge component is pieced together from the Asthma and Allergy Foundation of America, Mayoclinic, and Verywell Health as well as our clinical collaborator. Whereas, the personalization aspect is derived from the patient’s history of asthma collected from the questionnaires and day-to-day conversations. The system has been evaluated by eight asthma clinicians and eight computer science researchers for chatbot quality, technology acceptance, and system usability. kBOT achieved an overall technology acceptance score of greater than 8 on an 11-point Likert scale and a mean System Usability Score (SUS) greater than 80 from both evaluation groups.t for Asthma		kBot is a knowledge-driven personalized chatbot system designed to continuously track medication adherence of pediatric asthmatic patients (age 8 to 15) and monitor relevant health and environmental data. The outcome is to help asthma patients self manage their asthma progression by generating trigger alerts and educate them with various self-management strategies. kBot takes the form of an Android application with a frontend chat interface capable of conversing both text and voice-based conversations and a backend cloud-based server application that handles data collection, processing, and dialogue management. The domain knowledge component is pieced together from the Asthma and Allergy Foundation of America, Mayoclinic, and Verywell Health as well as our clinical collaborator. Whereas, the personalization aspect is derived from the patient’s history of asthma collected from the questionnaires and day-to-day conversations. The system has been evaluated by eight asthma clinicians and eight computer science researchers for chatbot quality, technology acceptance, and system usability. kBOT achieved an overall technology acceptance score of greater than 8 on an 11-point Likert scale and a mean System Usability Score (SUS) greater than 80 from both evaluation groups.t for Asthma