PREDOSE - Revision history

Farahnaz: /* People */

2019-05-02T23:25:22Z

‎People

Farahnaz: /* Related Projects */

2017-07-25T16:40:23Z

‎Related Projects

Farahnaz: /* Related Projects */

2017-07-25T16:39:58Z

‎Related Projects

Farahnaz: /* Related Projects */

2017-07-25T16:39:37Z

‎Related Projects

Farahnaz: /* Related Projects */

2017-07-25T16:37:48Z

‎Related Projects

Farahnaz: /* Stage 2: Automatic Qualitative Coding */

2017-07-25T16:36:52Z

‎Stage 2: Automatic Qualitative Coding

Farahnaz: /* Stage 2: Automatic Qualitative Coding */

2017-07-24T23:03:28Z

‎Stage 2: Automatic Qualitative Coding

Farahnaz: /* Publications */

2017-07-24T22:23:08Z

‎Publications

Farahnaz: /* Publications */

2017-07-24T22:22:21Z

‎Publications

Farahnaz at 17:37, 9 July 2017

2017-07-09T17:37:55Z

@@ Line 10: / Line 10: @@
 Co-Investigators: [http://www.med.wright.edu/citar/robertcarlson.html Robert Carlson]<br />
 External Collaborators: [http://www.umassmed.edu/emed/faculty/boyer.aspx Edward Boyer] (University of Massachussetts, Amherst)<br /><!-- -->
-Graduate Students: [http://knoesis.wright.edu/farahnaz/ Farahnaz Golroo], [http://knoesis.wright.edu/researchers/pavan/ Pavan Kapanipathi], [http://knoesis.wright.edu/researchers/sujan/  Sujan Perera], [http://knoesis.wright.edu/researchers/sanjaya/ Sanjaya Wijeratne], [http://knoesis.wright.edu/researchers/luchen/ Lu Chen], [http://knoesis.wright.edu/researchers/alan/ Gary A. Smith],  [http://knoesis.org/researchers/Nishita/ Nishita Jaykumar], [http://knoesis.wright.edu/researchers/swapnil/ Swapnil Soni] <br />
+Researchers: [http://knoesis.wright.edu/farahnaz/ Farahnaz Golroo], [http://knoesis.wright.edu/researchers/pavan/ Pavan Kapanipathi], [http://knoesis.wright.edu/researchers/sujan/  Sujan Perera], [http://knoesis.wright.edu/researchers/sanjaya/ Sanjaya Wijeratne], [http://knoesis.wright.edu/researchers/luchen/ Lu Chen], [http://knoesis.wright.edu/researchers/alan/ Gary A. Smith],  [http://knoesis.org/researchers/Nishita/ Nishita Jaykumar], [http://knoesis.wright.edu/researchers/swapnil/ Swapnil Soni] <br />
 Past Members: [http://knoesis.wright.edu/researchers/delroy/ Delroy Cameron], [http://knoesis.org/researchers/revathy Revathy Krishnamurthy], [http://knoesis.org/researchers/gaurish/ Gaurish Anand], [http://www.med.wright.edu/CITAR/RusselFalck Russel Falck] (Co-Investigator), [http://www.wright.edu/~kera.watkins/ Kera Z. Watkins] (Post Doc), Drashti Dave (Visiting Researcher), [http://pablomendes.com Pablo N. Mendes],  [http://knoesis.wright.edu/researchers/matthan Matthan Sink], [http://knoesis.wright.edu/researchers/michael Michael Cooney], Mandeep Singh, Pratik Desai, Mary Oberer, Kaustav Saha''

@@ Line 99: / Line 99: @@
 # [[EDrugTrends]]
 # [http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support]
-# [[DAO (Drug Abuse Ontology)]]
+# [[DAO]]
 =Funding=

@@ Line 99: / Line 99: @@
 # [[EDrugTrends]]
 # [http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support]
-# [DAO (Drug Abuse Ontology)]
+# [[DAO (Drug Abuse Ontology)]]
 =Funding=

@@ Line 99: / Line 99: @@
 # [[EDrugTrends]]
 # [http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support]
-# [[Drug Abuse Ontology (DAO)]]
+# [DAO (Drug Abuse Ontology)]
 =Funding=

@@ Line 99: / Line 99: @@
 # [[EDrugTrends]]
 # [http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support]
 =Funding=

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 ====Stage 2: Automatic Qualitative Coding====
 This is the most challenging aspect of PREDOSE. The aim is to use various information extraction techniques to extraction semantic information considered semantically equivalent to qualitative codes, from web forums. Types of extracted information include:
-#''' Drug Abuse Ontology (DAO):''' We manually created a Drug Abuse Ontology (DAO) to model the prescription drug abuse domain, which is the first ontology on drug abuse in the literature. The current DAO is available [http://knoesis-hpco.cs.wright.edu/drug-abuse-ontology/ online]. The DAO is used to facilitate search, and it also serves as the annotation scheme for entity, relationship and triple extraction. <br />
+#''' Drug Abuse Ontology (DAO):''' We manually created a [http://wiki.knoesis.org/index.php/DAO Drug Abuse Ontology (DAO)] to model the prescription drug abuse domain, which is the first ontology on drug abuse in the literature. The current DAO is available [http://knoesis-hpco.cs.wright.edu/drug-abuse-ontology/ online]. The DAO is used to facilitate search, and it also serves as the annotation scheme for entity, relationship and triple extraction. <br />
 # '''Entity Identification:''' from web forum data is challenging because web forums discussions are informal in nature. In particular, web forum data is characterized by a proliferation of slang term references to standard drug references. We leveraged mappings for slang term to known drugs from NIDA, NDCP, Erowid, Urban Dictionary etc to enhance our domain knowledge, model. However, while such mappings are a good starting point for entity identification, the more challenging issue of entity disambiguation requires more rigorous techniques. Entity disambiguation is necessary in three scenarios: 1) standard dictionary word disambiguation (e.g. girl as Gender or the drug Cocaine); 2) word sense disambiguation (i.e., done as Methadone or the act of being done with a task) and finally 3) concept reference disambiguation (i.e. the term "Oxy" may refer to Oxycontin, Generic Oxycontin, Oxycontin OP or Oxycontin OC). We have used a combination of lexical, linguistics and semantics-based techniques to address entity identification and disambiguation: the results of which are reported in our JBI Journal article.<ref name="jbi-13"> [http://knoesis.wright.edu/researchers/delroy/ D. Cameron], [http://knoesis.wright.edu/researchers/alan/ G. A. Smith], [http://www.med.wright.edu/CITAR/Daniulaityte R. Daniulaityte], [http://knoesis.wright.edu/amit/ A. P. Sheth], D. Dave, [http://knoesis.wright.edu/researchers/luchen/ L. Chen], [http://knoesis.wright.edu/researchers/gaurish/ G. Anand], [http://www.med.wright.edu/citar/robertcarlson.html R. Carlson], [http://sites.google.com/site/kzwscv K. Z. Watkins], R. Falck. [http://knoesis.org/library/resource.php?id=1792 PREDOSE: A Semantic Web Platform for Drug Abuse Epidemiology using Social Media] [http://nadir.uc3m.es/alejandro/pubs/sijbi.html Journal of Biomedical Informatics]. July 2013 [http://www.sciencedirect.com/science/article/pii/S1532046413001081 ScienceDirect] [[http://www.ncbi.nlm.nih.gov/pubmed/23892295 PMID 23892295]]</ref>
 #''' Relationship Extraction:''' We have utilized a lexical and semantics-based technique for relationship identification; the details of which are reported in our JBI Journal article. <ref name ="jbi-13" /><br />

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 =Publications=
 * [http://www.med.wright.edu/CITAR/Daniulaityte R. Daniulaityte], [http://www.med.wright.edu/citar/robertcarlson.html R. Carlson], G. Brigham, [http://knoesis.wright.edu/researchers/delroy/ D. Cameron], [http://knoesis.wright.edu/amit/ A. P. Sheth]. [http://knoesis.org/sites/default/files/FINAL%20Progress%20Report.pdf A Study of Social Web Data on Buprenorphine Abuse Using Semantic Web Technology]. Final Report.
-* [http://knoesis.wright.edu/researchers/delroy/ D. Cameron], [http://knoesis.wright.edu/researchers/alan/ G. A. Smith], [http://www.med.wright.edu/CITAR/Daniulaityte R. Daniulaityte], [http://knoesis.wright.edu/amit/ A. P. Sheth], D. Dave, [http://knoesis.wright.edu/researchers/luchen/ L. Chen], [http://knoesis.wright.edu/researchers/gaurish/ G. Anand], [http://www.med.wright.edu/citar/robertcarlson.html R. Carlson], [http://sites.google.com/site/kzwscv K. Z. Watkins], R. Falck. [http://knoesis.org/library/resource.php?id=1792 PREDOSE: A Semantic Web Platform for Drug Abuse Epidemiology using Social Media] [http://nadir.uc3m.es/alejandro/pubs/sijbi.html Journal of Biomedical Informatics]. July 2013 [http://www.sciencedirect.com/science/article/pii/S1532046413001081 ScienceDirect] [[http://www.ncbi.nlm.nih.gov/pubmed/23892295 PMID 23892295]]</ref>
+* [http://knoesis.wright.edu/researchers/delroy/ D. Cameron], [http://knoesis.wright.edu/researchers/alan/ G. A. Smith], [http://www.med.wright.edu/CITAR/Daniulaityte R. Daniulaityte], [http://knoesis.wright.edu/amit/ A. P. Sheth], D. Dave, [http://knoesis.wright.edu/researchers/luchen/ L. Chen], [http://knoesis.wright.edu/researchers/gaurish/ G. Anand], [http://www.med.wright.edu/citar/robertcarlson.html R. Carlson], [http://sites.google.com/site/kzwscv K. Z. Watkins], R. Falck. [http://knoesis.org/library/resource.php?id=1792 PREDOSE: A Semantic Web Platform for Drug Abuse Epidemiology using Social Media] [http://nadir.uc3m.es/alejandro/pubs/sijbi.html Journal of Biomedical Informatics]. July 2013
-#''' Relationship Extraction:''' We have utilized a lexical and semantics-based technique for relationship identification; the details of which are reported in our JBI Journal article. <ref name ="jbi-13" /><br />
 =Related=

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-'''PREDOSE''' is the acronym for '''PRE'''scription '''D'''rug abuse '''O'''nline '''S'''urveillance and '''E'''pidemiology, which is an inter-disciplinary project between the [http://knoesis.org Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] and the [http://www.med.wright.edu/citar/ Center for Interventions, Treatment and Addictions Research (CITAR)] at [https://www.wright.edu/ Wright State University]. The overall aim of PREDOSE is to develop techniques to facilitate prescription drug abuse epidemiology, related to the illicit use of pharmaceutical opioids. PREDOSE is designed to capture the knowledge, attitudes and behaviors of prescription drug abusers through the automatic extraction of semantic information ''(including entities, relationships, triples and other intelligible constructs such as sentiments, emotions, intervals, frequency, dosage, etc.'') from social media. PREDOSE is the predecessor of both the [http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] and [http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) iN3] projects.
+'''PREDOSE''' is the acronym for '''PRE'''scription '''D'''rug abuse '''O'''nline '''S'''urveillance and '''E'''pidemiology, which is an inter-disciplinary project between the [http://knoesis.org Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)] and the [http://www.med.wright.edu/citar/ Center for Interventions, Treatment and Addictions Research (CITAR)] at [https://www.wright.edu/ Wright State University]. The overall aim of PREDOSE is to develop techniques to facilitate prescription drug abuse epidemiology, related to the illicit use of pharmaceutical opioids. PREDOSE is designed to capture the knowledge, attitudes, and behaviors of prescription drug abusers through the automatic extraction of semantic information ''(including entities, relationships, triples and other intelligible constructs such as sentiments, emotions, intervals, frequency, dosage, etc.'') from social media. PREDOSE is the predecessor of both the [http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] and [http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) iN3] projects.
 =News=
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 In PREDOSE, we developed various components for Content Analysis. These components are included in the PREDOSE web application and the web application developed for Knowledge-Aware Search. More specifically, the PREDOSE Web Application contains components for: 1) Content Analysis and 2) spatio-temporal-thematic analysis.
-# '''Template Pattern Explorer''' This is a pattern-based component for information retrieval from unstructured texts that; 1) leverages background knowledge to identify lexical variants of ontological concepts in text; 2) has the ability to semantically interpret domain specific elements (e.g. dosage, frequency of use etc) not modeled in background knowledge; 3) enables finding associations in text between template classes based on proximity, by specifying template patterns (e.g. ''DRUG:DOSAGE:SIDEEFFECT'') <br />
+# '''Template Pattern Explorer''' This is a pattern-based component for information retrieval from unstructured texts that; 1) leverages background knowledge to identify lexical variants of ontological concepts in text; 2) has the ability to semantically interpret domain specific elements (e.g. dosage, frequency of use etc) not modeled in background knowledge; 3) enables finding associations in text between template classes based on proximity, by specifying template patterns (e.g. ''DRUG: DOSAGE:SIDEEFFECT'') <br />
 # '''Custom (Proximity) Search''' This component is a flexible lightweight extension of the Template Pattern Explorer that facilitates pattern-based search, using ontological concepts and user-specified keywords in close proximity, configurable at runtime. <br />
-# '''Content Explorer''' is a broad content exploration and annotation environment for content analysis. The exploration component enables analysis of text content restricted by: 1) ontological concepts; 2) user-specified keywords; 3) specific data sources and 4) user-specified time ranges. The annotation component supports the creation of training data for information extraction tasks such as: 1) entity identification and 2) sentiment extraction ubiquitous to the project. <br />
+# '''Content Explorer''' is a broad content exploration and annotation environment for content analysis. The exploration component enables analysis of text content restricted by 1) ontological concepts; 2) user-specified keywords; 3) specific data sources and 4) user-specified time ranges. The annotation component supports the creation of training data for information extraction tasks such as 1) entity identification and 2) sentiment extraction ubiquitous to the project. <br />
 # '''Trend Explorer''' is a component for longitudinal data analysis based on statistical aggregation of ontological concept mentions and sentiment expressions occurring text based on frequency counts and user activity. <br />
-# '''Emerging Patterns Explorer''' is an extension of the Trend Explorer for trend analysis of concomitantly occurring ontological concepts and user-specified keywords.  This component is most significant because the ability to detect spikes in discussions based on frequently co-occurring terms, unbeknownst to researchers.
+# '''Emerging Patterns Explorer''' is an extension of the Trend Explorer for trend analysis of concomitantly occurring ontological concepts and user-specified keywords.  This component is most significant because of the ability to detect spikes in discussions based on frequently co-occurring terms, unbeknownst to researchers.
 A detailed description of the PREDOSE platform is available in our recently published paper in the Journal of Biomedical Informatics. <ref name="jbi-13" /> Insights into patterns and trends of Buprenorphine use are under review in the literature<ref name="cpdd-14">[http://www.med.wright.edu/CITAR/Daniulaityte R. Daniulaityte], [http://www.med.wright.edu/citar/robertcarlson.html R. Carlson], [http://knoesis.wright.edu/researchers/delroy D. Cameron], [http://knoesis.wright.edu/researchers/alan/ G. A. Smith], [http://knoesis.wright.edu/amit A. P. Sheth], [http://www.knoesis.org/library/resource.php?id=1975 When less is more: A web-based study of user beliefs about buprenorphine dosing in self-treatment of opioid withdrawal symptoms]. The College on Problems of Drug Dependence [http://www.cpdd.vcu.edu/ CPDD 2014], San Juan, Puerto Rico, June 14-17, 2014</ref><ref name="dad-14"> [http://www.med.wright.edu/CITAR/Daniulaityte R. Daniulaityte], [http://www.med.wright.edu/citar/robertcarlson.html R. Carlson], G. Brigham, [http://knoesis.wright.edu/researchers/delroy/ D. Cameron], [http://knoesis.wright.edu/amit/ A. P. Sheth]. [http://knoesis.org/?q=node/2193 "Sub is a weird drug:" A Web-based study of lay attitudes about use of buprenorphine to self-treat opioid withdrawal symptoms]. American Journal of Addictions, 2015; 24(5):403-409. [[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527156/?tool=mybib, PMC 4527156]]</ref>