Difference between revisions of "PREDOSE"

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Druoap is the name of the NIH funded '''DRU'''g '''A'''buse '''O'''ntology '''P'''roject (July 2011 - present) which is an inter-disciplinary collaborative project between the Ohio Center for Excellence in Knowledge-enabled Computing (Kno.e.sis) and the Center for Treatment, Interventions and Addictions Research (CITAR) at Wright State University. The overall aim of Druaop is to develop automated mechanisms for web forum data analysis related to the illicit use of pharmaceutical opioids.
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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.  
  
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=Introduction=
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=News=
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The non-medical use of pharmaceutical opioids has been identified as one of the fastest growing forms of drug abuse in the U.S. Furthermore, significant increases in the illicit use of pharmaceutical opioids have expanded the pathways to heroin addiction and resulted in escalating rates of accidental overdose deaths. To design effective and responsive prevention and policy measures, public health professionals require timely and reliable information on new and emerging drug trends. Although existing epidemiological data systems provide critically important information about drug abuse trends, they are often time-lagged. There is therefore a need for epidemiological sources that could complement existing drug trend monitoring systems and enhance their capacity for early identification of new and emerging trends. The World Wide Web (Web) has been identified as one of the leading data sources for detecting patterns and changes in the non-medical use of pharmaceutical and other illicit drugs. Many Web 2.0 empowered social platforms, including Web forums, provide venues for individuals to freely share their experiences, post questions, and offer comments about different drugs.  
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* [http://www.med.wright.edu/whatsnew/2013/socialweb Researchers use social web forum data to understand nonmedical use of painkillers] (article by [http://www.med.wright.edu/ Wright State University School of Medicine] May 6, 2013)
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* [http://semanticweb.com/semantic-app-helps-researchers-understand-prescription-drug-abuse_b29788 Semantic App Helps Researchers Understand Prescription Drug Abuse] (article on [http://www.semanticweb.com Semanticweb.com] June 11, 2012)
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* [http://www.med.wright.edu/citar/web-based-UGC Social Web Data on Buprenorphine Abuse Using Semantic Web Technology] (article by [http://www.med.wright.edu/ Wright State University School of Medicine])
  
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=People=
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Principal Investigators: [http://www.med.wright.edu/CITAR/Daniulaityte Raminta Daniulaityte], [http://knoesis.wright.edu/amit Amit P. Sheth] <br />
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Co-Investigators: [http://www.med.wright.edu/citar/robertcarlson.html Robert Carlson]<br />
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External Collaborators: [http://www.umassmed.edu/emed/faculty/boyer.aspx Edward Boyer] (University of Massachussetts, Amherst)<br /><!-- -->
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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 />
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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''
  
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'''This project aims to address this critical need for relevant and timely information by pursuing two(2) specific goals:'''
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=Overview=
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The non-medical use of pharmaceutical opioids has been identified as one of the fastest growing forms of drug abuse in the U.S. The [http://en.wikipedia.org/wiki/Office_of_National_Drug_Control_Policy White House Office of National Drug Control Policy (ONDCP)] in May 2011, launched the [http://www.healthnews.com/en/news/US-Targeting-Prescription-Drug-Abuse/0DFqFbmBD1ref$CoJ1D5XR/ ''Epidemic: Responding to America’s Prescription Drug Abuse Crisis''] initiative to curb prescription drug abuse problem, mainly through education and drug monitoring programs. This White House Initiative has been prompted by recent research which associate the rise in prescription drug abuse with two important phenomena: 1) expanded pathways to heroin addiction and 2) escalating rates of accidental overdose deaths. To combat these trends, public health professionals require timely and reliable information on new and emerging patterns and trends in prescription drug abuse.
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Although existing epidemiological data systems provide critically important information about drug abuse trends, they are often time-lagged. Hence, there is a critical need for content analysis platforms that could complement existing drug abuse monitoring systems and enhance the overall capacity for early identification of new and emerging patterns and trends. The World Wide Web (Web) has been identified as one of the leading data sources for detecting patterns and changes in the non-medical use of pharmaceutical and other illicit drugs. Many Web 2.0 empowered social media platforms, including web forums and tweets, provide avenues for individuals to freely share their experiences, post questions, and offer comments about various drugs. The PREDOSE project is designed to extract and analyze semantic information from online web forum discussions, as a means of detecting timely emerging patterns and trends in the non-medical use of pharmaceutical opioids. '''The PREDOSE project therefore has two(2) specific aims:'''
  
 
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# To determine user knowledge attitudes and behavior related to the non-medical use of pharmaceutical opioids (namely buprenorphine) as discussed on Web-based forums
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# To determine user knowledge, attitudes and behavior related to the non-medical use of pharmaceutical opioids (namely buprenorphine) as discussed on Web-based forums
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# To determine spatio-temporal trends and patterns in pharmaceutical opioid abuse as discussed on Web-based forums
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# To determine spatio-temporal-thematic patterns and trends in pharmaceutical opioid abuse as discussed on Web-based forums
 
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Principal Investigators: Raminta Daniulaityte, [http://knoesis.wright.edu/amit Amit P. Sheth] <br />
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Prescription drug abuse research typically rely on manual data collection and annotation. Data are commonly gathered from interactive interviews with individual or groups of drug users. Interviews are transcribed into text, which are then manually annotated <i>(or coded)</i> with abstract themes. This process of  '''qualitative coding''' is often facilitated using qualitative research software, such as [http://www.qsrinternational.com/products_nvivo.aspx NVivo], for ''Content Analysis''. However, the intensive manual effort required for coding is not scalable and therefore impractical for Web-based data. Moreover, Web-based texts are fraught with grammatical errors, misspellings and slang, which can be laborious to interpret. To effectively process the large volume of abstruse heterogeneous Web-based data available from web forums, the field requires a highly automated way of extracting meaningful information from such texts, not limited to entities, sentiments, relationships and triples,
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Co-Investigators: [http://www.med.wright.edu/citar/robertcarlson.html Robert Carlson], Russel Falck <br />
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Graduate Students: [http://knoesis.wright.edu/researchers/delroy/ Delroy Cameron], [http://knoesis.wright.edu/researchers/sujan/  Sujan Udayanga]
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   |title= Project Overview
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   |title= Approach
 
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'''Problem:''' Historically, qualitative research has been characterized by manual data collection, initiated by interactive interview sessions with individual or a group of individual addicts. The audio-to-text transcribed interviews obtained from this process are then typically annotated by researchers/experts with themes or topics that surfaced during interview sessions. This process is called '''qualitative coding'''. Various tools, such as ... have been developed to facilitate this annotation process, and provide additional service such as search, retrieval and data analysis. However, the intensive manual effort required to make the interactive approach scalable is enormous. Furthermore, to effectively process the large volume and complexity of the Web-based data, the field certainly needs a highly automated way of accessing and processing Web data.
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To automate the extraction of semantic information from Web-based data, researchers from the Kno.e.sis Center at Wright State University are building information extraction techniques applied in prior research. In past research, lexical, linguistics-based, pattern-based and semantics-based processing techniques applied have been applied  to <u>automatically extract knowledge</u> from structured biomedical texts, Wikipedia Articles, and social media (i.e., tweets). Kno.e.sis researchers have also made substantial progress in <understanding the content to: 1) identify social perceptions; 2) generate personalized information streams; 3) provide coordination and 4) identify sentiment and emotions from informal texts from MySpace, Facebook, and Twitter. Adaptations to  these information processing techniques have been made to accommodate complex web forum discussions, for trend and pattern detection in prescription drug abuse research.
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'''Proposed Solution:''' Researchers at the Kno.e.sis Center at Wright State University have successfully applied Semantic Web, Machine Learning and Natural Language Processing techniques to <u>automatically extract knowledge</u> from biomedical text. Substantial progress has also been made in using these and other techniques to <u>understand the content and identify social perceptions</u> through metadata extraction and spatio-temporal and thematic analysis (broadly termed semantic analysis) of <u>informal text</u> on MySpace, Facebook, and Twitter. These cutting-edge information processing techniques, with appropriate adaptations can now be exploited to fit the needs of public health and drug abuse research.
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   |content= The overall research plan has three(3) distinct stages: the first stage is the '''Data Collection''' stage, which is an intended alternative to manually conducted interviews. It operates under the assumption that information gathered from interview sessions are expressed in online forums and therefore, data crawling software can be used to collect data from web sources instead of laborious interviews as the means of obtaining qualitative data. The second stage is the process of '''Automatic Qualitative Coding'''. Through entity identification, relationship identification and complete triple extraction, this process aims at capturing the semantics of information expressed in the web forum data, with acceptable levels of precision and recall. The complete range of techniques, including pattern-based, statistical probabilistic and semantics-based analysis will play a critical role in this phase. The final stage is '''Data Analysis & Interpretation''' of the RDF data (i.e. Drug Abuse Ontology - DAO) collected from phase 2 using existing semantic web tools at Kno.e.sis or new tools to be developed where appropriate.
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   |content= The overall research plan of the PREDOSE platform consists of three(3) stages:  
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# '''Data Collection:''' Kno.e.sis researchers have developed custom web crawlers that collect data from select web forums identified for this study. Raw data are collected, cleaned and stored in databases for processing.<br />
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# '''Automatic Qualitative Coding:''' The PREDOSE research team has developed preliminary techniques that automatically extract semantic information from Web-based data. Such includes entities, generic sentiment expressions,  relationships and triples. To perform entity identification, the research team relies on  a combination of lexical and semantics-based techniques, based on a manually curated [http://knoesis-hpco.cs.wright.edu/drug-abuse-ontology/ Drug Abuse Ontology] (DAO) - ''pronounced dow''), which is the first ontology for prescription drug abuse. To extract relationships the PREDOSE team has implemented a lexical and semantics-based technique applies a semantic similarity measure between relationship candidates, WordNet Synsets and predicates from the UMLS. For triple extraction the team has implemented a top-down pattern-based approach using DAO patterns, and the [http://researcher.watson.ibm.com/researcher/view_project.php?id=1264 SystemT] framework to extract triple patterns from text.<br /> An optimization algorithm for sentiment extraction has also been applies to identify generic sentiment expressions.
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# '''Data Analysis & Interpretation:''' PREDOSE provides various tools to facilitate analysis of extracted information, including a: 1)  ''Template Pattern Explorer'' ([http://knoesis-hpco.cs.wright.edu/knowledge-aware-search/ beta]); 2)'' Custom (Proximity) Search''; 3) ''Content Explorer''; 4) ''Trend Explorer'' and 5) ''Emerging Patterns Explorer.'' These tools are currently showcased in a [http://knoesis-hpco.cs.wright.edu/predose/ beta web application] ([https://www.youtube.com/watch?v=gCFPzMgEPQM video demo]).  Figure 1 shows the overall architecture of the PREDOSE platform.
 
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[[Image:Citar-research-plan-071811.png | center | 600px | thumb | Fig1: Research Plan]]
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[[Image:PREDOSE-architecture.png | center | 600px | thumb | Fig1: Research Plan]]
  
 
====Stage 1: Data Collection====
 
====Stage 1: Data Collection====
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*'''Web Site Selection: ''' Web forums selected for the study are chosen based on the following criteria 1) they allow free discussion of psychoactive drug use; 2) contain information on illicit pharmaceutical drug use, and 3) are publicly accessible. Additionally, since it is important that this study collects relevant and timely information, such forums are also expected to be very active both in terms of number of users and topics of discussion.
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#'''Web Forum Selection: ''' The first component in the PREDOSE platform in stage 1 is for data collection. Web forums selected for the study were chosen based on the following criteria the web forum: 1) allows free discussion of psychoactive drug use; 2) contains information on illicit pharmaceutical drug use, and 3) is publicly accessible. Further, since it is important that this study collects relevant and timely information, such forums are also considered active, both in terms of number of users and diversity in topic discussions.<br />
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*'''Web Crawling: ''' Various popular HTML parsers (e.g. Nutch, Jericho HTML Parser etc) exist for parsing web data. Data crawling periodically is necessary to update our databases with the most recent data published by the selected sources. Standardized web forum software somewhat alleviate the traditional problems involved with mining web data. The use of such software enable exploitation of the structure of web forum site by our custom crawlers.
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#'''Web Crawling: ''' HTML parsers are publicly available to crawl web sites and collect data. Some of these include Nutch, Jericho HTML Parser, HTMLParser etc. In PREDOSE we use the Jericho HTML Parser to write Custom Web Crawlers to crawl data from three online web forums to obtain data for analysis. <br />
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*'''Data Cleaning: ''' One of the most challenging problems in dealing with web data is decoding special HTML characters to obtain ASCII text and separating special characters from standard text. 
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#'''Data Cleaning: ''' We sanitize the crawled HTML and decode special characters in a data cleaning phase that occurs throughout our application where necessary. <br />
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*''' Location Resolution:''' Collection location data is important for spatio-temporal-thematic analysis. It would not be surprising that drug abuse practices across continent with regard to some specific drugs (e.g. heroin) will vary vastly. The most anticipated variations are likely in drug mixtures. For example, it may be popular culture to use heroin+cocaine in one region, while this practice is entirely uncommon in another.
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#'''Informal Text Database:''' Crawled data is stored in a MySQL database together with an index for fast retrieval. We mainly store semantic metadata in the database, based on our information extraction techniques.
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* '''Informal Text Database:''' It is necessary to collect and store a wide selection on data for this study. Some database tables ''include, users, posts, source and location (city, state, country, continent, zip).''
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====Stage 2: Automatic Qualitative Coding====
 
====Stage 2: Automatic Qualitative Coding====
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This is the most challenging aspect of this project. The aim is to use various information extraction techniques to extraction triples from web forum data. Such extraction is to be undertaken in three steps:
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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:
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* '''Entity Identification:''' The most challenging aspect of entity identification from web forum data is the informal nature of the text. Web forum data is characterized by a proliferation of slang terms instead of standard references to known drugs. Fortunately, slang term to known drug mappings are available online through various source, such as (NIDA, NDCP, Erowid, Urban Dictionary etc). We exploit these sources as a starting point for recognizing slang terms that reference known drugs. However, these mappings create unfortunate side effect of ambiguity. "Oxy" can refer to Oxycontin, Generic Oxycontin, Oxycontin OP or Oxycontin OC. Hence, some techniques for slang term disambiguation become necessary. We have so far taken a probabilistic approach to entity disambiguation, since the surround terms to an ambiguous slang term are also slang and therefore do not help semantics-based approach that leverage the ontology schema.  
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#''' 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 />
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*''' Relationship Extraction:''' We anticipate that the success of our entity extraction along with Drug Abuse Ontology schema will directly impact the relationship extraction. However alternative relationship extraction have been covered elsewhere and will be adapted where appropriate.
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# '''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>
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*''' Triple Extraction:''' Previous work in the lab have successfully implemented rule-based triple extraction (Ramakrishnan C, Mendes P. N. etc) on structured biomedical literature. In other work, (Thomas C, Mehra P, etc) have implemented a statistical/probabilistic approach to triple extraction also on structured text. Such techniques are not likely apply to informal web forum text. Hence, one approach is to translate our informal text into structured text, once entities and relationships have been identified. Alternatively, standard-alone pattern-based, probabilistic and semantics-based techniques can be used to complete triple extraction based on the effectiveness of the entity and relationship extraction.
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#''' 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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*''' Drug Abuse Ontology (DAO):''' The final output of the triple extraction is population of the Drug Abuse Ontology instance base. This, together with the DAO schema, we intend to maintain as a dynamic ontology created from user-generated content (UGC).
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#''' Triple Extraction:''' Previous work at Kno.e.sis have successfully implemented rule-based and probabilistic approaches to triple extraction (Ramakrishnan C, Mendes P. N. and Thomas C. Mehra P), albeit on structured biomedical literature. In another approach Thomas C and Mehra P, etc have implemented a statistical/probabilistic approach to triple extraction also on structured text. Such techniques are not likely apply to informal web forum text. Hence, we implemented a top-down pattern-based technique for triple extraction that utilizes the DAO and the declarative information extraction framework SystemT and it's implementation language [http://pic.dhe.ibm.com/infocenter/bigins/v1r2/index.jsp?topic=%2Fcom.ibm.swg.im.infosphere.biginsights.doc%2Fdoc%2Fbiginsights_aqlref_con_aql-overview.html AQL (Annotation Query Language)], borrowing from our previous research on pattern-based information extraction from unstructured text<ref>[http://knoesis.wright.edu/researchers/delroy D. Cameron], V. Bhagwan, [http://knoesis.wright.edu/amit/ A. P. Sheth], [http://knoesis.org/library/resource.php?id=1781 Towards Comprehensive Longitudinal Healthcare Data Capture]. In The 1st International Workshop on the role of Semantic Web in Literature-Based Discovery, [http://knoesis.org/swlbd2012/ SWLBD2012] (co-located with the IEEE International Conference on Bioinformatics and Biomedicine, [http://www.ischool.drexel.edu/ieeebibm/bibm12/ BIBM2012]) Philadelphia PA USA, October 4, 2012, p. 241-247</ref>. <br />
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#'''Sentiment Extraction''' - We use an adaptation of the state-of-the-art sentiment extraction extraction technique developed by Chen et al<ref>[http://knoesis.wright.edu/researchers/luchen/ Lu Chen], [http://knoesis.wright.edu/researchers/wenbo/ Wenbo Wang], [http://researcher.watson.ibm.com/researcher/view.php?person=us-MeenaNagarajan Meenakshi Nagarajan], [http://knoesis.wright.edu/faculty/swang/ Shaojun Wang] and [http://knoesis.wright.edu/amit/ Amit P. Sheth]. [http://www.knoesis.org/library/resource.php?id=1689 Extracting Diverse Sentiment Expressions with Target-dependent Polarity from Twitter.] In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM), 2012.</ref> to extraction on-target sentiment expressions from web forum data. <br />
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#'''Template Pattern Identification''' - We use a '''context-free grammar''' <ref>[http://knoesis.wright.edu/researchers/delroy/ D. Cameron], [http://knoesis.wright.edu/amit/ A. P. Sheth], [http://knoesis.org/researchers/Nishita/ N. Jaykumar], [http://knoesis.wright.edu/researchers/gaurish/ G. Anand], [http://knoesis.wright.edu/tkprasad/ K.Thirunarayan], [http://knoesis.wright.edu/researchers/alan/ G. A. Smith]. [http://knoesis.org/library/resource.php?id=1874 A Hybrid Approach to Finding Relevant Social Media Content for Complex Domain Specific Information Needs] Journal of Web Semantics. 29: 39-52. 2014. </ref> to define the query language of strings interpretable by PREDOSE. This is a necessary task since many of the complex information needs in PREDOSE require a knowledge of ontological concepts as well as concepts not defined in ontologies such as emotion, sentiment, intensity, frequency, dosage intervals etc.
  
 
====Stage 3: Data Analysis & Interpretation====
 
====Stage 3: Data Analysis & Interpretation====
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*''' Semantic Web Tools: ''' Many tools for data analysis exist at Kno.e.sis. Some of these include, 1) Twitris for spatio-temporal-thematic analysis 2) Cuebee for automatic complex query creation over RDF data and 3) Scooner for guided navigation of documents annotated with semantic metadata (entities or triples). Once the DAO has been created, the data can be easily infused into any of these tools to support analysis. Alternatively, new tools can be created on demand.
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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.
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*''' Spatio-Temporal-Thematic Analysis:''' Discussion on the integration of web forum data into Twitris has already begun. Owing to the use of the slang term dictionary, qualitative researchers will be able to observe posts contains easily identifiable and non-ambiguous references to known drugs in various locations.  
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# '''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 />
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# '''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 />
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# '''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 />
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# '''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 />
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# '''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.
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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>
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=Loperamide-Withdrawal Discovery=
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In the early stages of the PREDOSE project we made a discovery, now reported in the literature<ref>[http://www.med.wright.edu/CITAR/Daniulaityte R. Daniulaityte], [http://www.med.wright.edu/citar/robertcarlson.html R. Carlson], [http://www.med.wright.edu/CITAR/RusselFalck R. Falck], [http://knoesis.wright.edu/researchers/delroy/ D. Cameron], [http://knoesis.org/researchers/sujan/ S. Perera], [http://knoesis.wright.edu/researchers/luchen/ L. Chen], [http://knoesis.wright.edu/amit/ A. P. Sheth]. [http://knoesis.org/library/resource.php?id=1790 "I Just Wanted to Tell You That Loperamide WILL WORK": A Web-Based Study of Extra-Medical Use of Loperamide]. [http://www.journals.elsevier.com/drug-and-alcohol-dependence/ Journal of Drug and Alcohol Dependence]. 130(1-3): 241-244, 2013. [http://www.sciencedirect.com/science/article/pii/S0376871612004292 ScienceDirect], [[http://www.ncbi.nlm.nih.gov/pubmed/23201175 PMID 23201175]]</ref> <ref>[http://www.med.wright.edu/CITAR/Daniulaityte R. Daniulaityte], [http://www.med.wright.edu/citar/robertcarlson.html R. Carlson], [http://www.med.wright.edu/CITAR/RusselFalck R. Falck], [http://knoesis.wright.edu/researchers/delroy/ D. Cameron], [http://knoesis.org/researchers/sujan/ S. Perera], [http://knoesis.wright.edu/researchers/luchen/ L. Chen], [http://knoesis.wright.edu/amit/ A. P. Sheth]. [http://www.knoesis.org/library/resource.php?id=1703 A Web-Based Study of Self-Treatment of Opioid Withdrawal Symptoms with Loperamide]. The College on Problems of Drug Dependence [http://www.cpdd.vcu.edu/ CPDD 2012], Palm Springs, CA USA, June 9-14, 2012.</ref>.<br />
 +
 
 +
Based on the lexical and semantics-based techniques for entity identification various datasets were isolated according to drug mentions, based on mapping slang references to standard concepts. In one dataset, it was observed that <i><font color="green">users reported taking the anti-diarrhea treatment drug Loperamide (sold over the counter in Imodium) to self-medicate from withdrawal symptoms.</font></i> The opioid addictions treatment drugs Buprenorphine and Methadone are commonly prescribed for treatment of withdrawal symptoms. Until now, it was unknown that Loperamide, can be (and is being) used for the same purpose. Which is more, it was observed that users reported the possibility of mild psychoactive (opiated) effects from ''megadosing'' - which is the practice of taking severely excessive amounts of a drug.
 +
 
 +
=PREDOSE Live=
 +
http://knoesis-hpco.cs.wright.edu/predose/ [[https://www.youtube.com/watch?v=gCFPzMgEPQM Video Demo]]<br />
 +
http://knoesis-hpco.cs.wright.edu/knowledge-aware-search [[http://www.youtube.com/watch?v=xadneDn0yXw Video Demo]] <br />
 +
 
 +
=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
 +
 
 +
=Related=
 +
# [http://www.med.wright.edu/whatsnew/2013/socialweb Researchers use social web forum data to understand nonmedical use of painkillers]
 +
# [http://www.healthnews.com/en/news/US-Targeting-Prescription-Drug-Abuse/0DFqFbmBD1ref$CoJ1D5XR/ U.S. Targeting Prescription Drug Abuse]
 +
# [http://www.nsf.gov/news/news_summ.jsp?cntn_id=121864&WT.mc Twitter Helps Determine "Morning People" and "Night Owls"]
 +
# [[Knowledge-Aware-Search]]
 +
 
 +
=Related Projects=
 +
# [http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)]
 +
# [[EDrugTrends]]
 +
# [http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support]
 +
# [[DAO]]
  
 
=Funding=
 
=Funding=
−
This project is sponsored by the National Institutes of Health (NIH) Grant Award No. R21 DA030571-01A1 to the Ohio Center for Excellence in Knowledge-enabled Computing (Kno.e.sis) and the Center for Treatment, Interventions and Addictions Research (CITAR) titled “A Study of Social Web Data on Buprenorphine Abuse using Semantic Web Technology.” Any opinions, findings, conclusions or recommendations expressed in this material are those of the investigator(s) and do not necessarily reflect the views of the National Institutes of Health.  
+
This project was initially sponsored by the National Institutes of Health (NIH) Grant No. [http://projectreporter.nih.gov/project_info_description.cfm?projectnumber=1R21DA030571-01A1 R21 DA030571-01A1] awarded to 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)] titled: '''A Study of Social Web Data on Buprenorphine Abuse using Semantic Web Technology'''. It is continued under National Institutes on Drug Abuse (NIDA) Grant No. R56DA038366-01, titled: <b>NIDA National Early Warning System Network (iN3):
 +
An Innovative Approach</b> <i>[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) (wiki page)]</i>. Any opinions, findings, conclusions or recommendations expressed in this material are those of the investigator(s) and do not necessarily reflect the views of the National Institutes of Health.  
 +
 
 +
Contact: [http://knoesis.wright.edu/researchers/farah/ Farahnaz Golroo]
  
−
Contact: [http://knoesis.wright.edu/researchers/delroy/ Delroy Cameron]
+
[[Category:Information Extraction]]
 +
[[Category:Semantic Search]]
 +
[[Category:Social Media]]
 +
[[Category:Text_Analytics]]
 +
[[Category:Data_Mining]]

Latest revision as of 23:25, 2 May 2019

PREDOSE is the acronym for PREscription Drug abuse Online Surveillance and Epidemiology, which is an inter-disciplinary project between the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) and the Center for Interventions, Treatment and Addictions Research (CITAR) at 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 eDrugTrends and iN3 projects.

News

People

Principal Investigators: Raminta Daniulaityte, Amit P. Sheth
Co-Investigators: Robert Carlson
External Collaborators: Edward Boyer (University of Massachussetts, Amherst)
Researchers: Farahnaz Golroo, Pavan Kapanipathi, Sujan Perera, Sanjaya Wijeratne, Lu Chen, Gary A. Smith, Nishita Jaykumar, Swapnil Soni
Past Members: Delroy Cameron, Revathy Krishnamurthy, Gaurish Anand, Russel Falck (Co-Investigator), Kera Z. Watkins (Post Doc), Drashti Dave (Visiting Researcher), Pablo N. Mendes, Matthan Sink, Michael Cooney, Mandeep Singh, Pratik Desai, Mary Oberer, Kaustav Saha

Overview

The non-medical use of pharmaceutical opioids has been identified as one of the fastest growing forms of drug abuse in the U.S. The White House Office of National Drug Control Policy (ONDCP) in May 2011, launched the Epidemic: Responding to America’s Prescription Drug Abuse Crisis initiative to curb prescription drug abuse problem, mainly through education and drug monitoring programs. This White House Initiative has been prompted by recent research which associate the rise in prescription drug abuse with two important phenomena: 1) expanded pathways to heroin addiction and 2) escalating rates of accidental overdose deaths. To combat these trends, public health professionals require timely and reliable information on new and emerging patterns and trends in prescription drug abuse.

Although existing epidemiological data systems provide critically important information about drug abuse trends, they are often time-lagged. Hence, there is a critical need for content analysis platforms that could complement existing drug abuse monitoring systems and enhance the overall capacity for early identification of new and emerging patterns and trends. The World Wide Web (Web) has been identified as one of the leading data sources for detecting patterns and changes in the non-medical use of pharmaceutical and other illicit drugs. Many Web 2.0 empowered social media platforms, including web forums and tweets, provide avenues for individuals to freely share their experiences, post questions, and offer comments about various drugs. The PREDOSE project is designed to extract and analyze semantic information from online web forum discussions, as a means of detecting timely emerging patterns and trends in the non-medical use of pharmaceutical opioids. The PREDOSE project therefore has two(2) specific aims:

Goals
  1. To determine user knowledge, attitudes and behavior related to the non-medical use of pharmaceutical opioids (namely buprenorphine) as discussed on Web-based forums
  2. To determine spatio-temporal-thematic patterns and trends in pharmaceutical opioid abuse as discussed on Web-based forums
Research Problem

Prescription drug abuse research typically rely on manual data collection and annotation. Data are commonly gathered from interactive interviews with individual or groups of drug users. Interviews are transcribed into text, which are then manually annotated (or coded) with abstract themes. This process of qualitative coding is often facilitated using qualitative research software, such as NVivo, for Content Analysis. However, the intensive manual effort required for coding is not scalable and therefore impractical for Web-based data. Moreover, Web-based texts are fraught with grammatical errors, misspellings and slang, which can be laborious to interpret. To effectively process the large volume of abstruse heterogeneous Web-based data available from web forums, the field requires a highly automated way of extracting meaningful information from such texts, not limited to entities, sentiments, relationships and triples,

Approach

To automate the extraction of semantic information from Web-based data, researchers from the Kno.e.sis Center at Wright State University are building information extraction techniques applied in prior research. In past research, lexical, linguistics-based, pattern-based and semantics-based processing techniques applied have been applied to automatically extract knowledge from structured biomedical texts, Wikipedia Articles, and social media (i.e., tweets). Kno.e.sis researchers have also made substantial progress in <understanding the content to: 1) identify social perceptions; 2) generate personalized information streams; 3) provide coordination and 4) identify sentiment and emotions from informal texts from MySpace, Facebook, and Twitter. Adaptations to these information processing techniques have been made to accommodate complex web forum discussions, for trend and pattern detection in prescription drug abuse research.

Research Plan

The overall research plan of the PREDOSE platform consists of three(3) stages:

  1. Data Collection: Kno.e.sis researchers have developed custom web crawlers that collect data from select web forums identified for this study. Raw data are collected, cleaned and stored in databases for processing.
  2. Automatic Qualitative Coding: The PREDOSE research team has developed preliminary techniques that automatically extract semantic information from Web-based data. Such includes entities, generic sentiment expressions, relationships and triples. To perform entity identification, the research team relies on a combination of lexical and semantics-based techniques, based on a manually curated Drug Abuse Ontology (DAO) - pronounced dow), which is the first ontology for prescription drug abuse. To extract relationships the PREDOSE team has implemented a lexical and semantics-based technique applies a semantic similarity measure between relationship candidates, WordNet Synsets and predicates from the UMLS. For triple extraction the team has implemented a top-down pattern-based approach using DAO patterns, and the SystemT framework to extract triple patterns from text.
    An optimization algorithm for sentiment extraction has also been applies to identify generic sentiment expressions.
  3. Data Analysis & Interpretation: PREDOSE provides various tools to facilitate analysis of extracted information, including a: 1) Template Pattern Explorer (beta); 2) Custom (Proximity) Search; 3) Content Explorer; 4) Trend Explorer and 5) Emerging Patterns Explorer. These tools are currently showcased in a beta web application (video demo). Figure 1 shows the overall architecture of the PREDOSE platform.
Fig1: Research Plan

Stage 1: Data Collection

  1. Web Forum Selection: The first component in the PREDOSE platform in stage 1 is for data collection. Web forums selected for the study were chosen based on the following criteria the web forum: 1) allows free discussion of psychoactive drug use; 2) contains information on illicit pharmaceutical drug use, and 3) is publicly accessible. Further, since it is important that this study collects relevant and timely information, such forums are also considered active, both in terms of number of users and diversity in topic discussions.
  2. Web Crawling: HTML parsers are publicly available to crawl web sites and collect data. Some of these include Nutch, Jericho HTML Parser, HTMLParser etc. In PREDOSE we use the Jericho HTML Parser to write Custom Web Crawlers to crawl data from three online web forums to obtain data for analysis.
  3. Data Cleaning: We sanitize the crawled HTML and decode special characters in a data cleaning phase that occurs throughout our application where necessary.
  4. Informal Text Database: Crawled data is stored in a MySQL database together with an index for fast retrieval. We mainly store semantic metadata in the database, based on our information extraction techniques.

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:

  1. 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 online. The DAO is used to facilitate search, and it also serves as the annotation scheme for entity, relationship and triple extraction.
  2. 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"> D. Cameron, G. A. Smith, R. Daniulaityte, A. P. Sheth, D. Dave, L. Chen, G. Anand, R. Carlson, K. Z. Watkins, R. Falck. PREDOSE: A Semantic Web Platform for Drug Abuse Epidemiology using Social Media Journal of Biomedical Informatics. July 2013 ScienceDirect [PMID 23892295]</ref>
  3. 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" />
  4. Triple Extraction: Previous work at Kno.e.sis have successfully implemented rule-based and probabilistic approaches to triple extraction (Ramakrishnan C, Mendes P. N. and Thomas C. Mehra P), albeit on structured biomedical literature. In another approach Thomas C and Mehra P, etc have implemented a statistical/probabilistic approach to triple extraction also on structured text. Such techniques are not likely apply to informal web forum text. Hence, we implemented a top-down pattern-based technique for triple extraction that utilizes the DAO and the declarative information extraction framework SystemT and it's implementation language AQL (Annotation Query Language), borrowing from our previous research on pattern-based information extraction from unstructured text<ref>D. Cameron, V. Bhagwan, A. P. Sheth, Towards Comprehensive Longitudinal Healthcare Data Capture. In The 1st International Workshop on the role of Semantic Web in Literature-Based Discovery, SWLBD2012 (co-located with the IEEE International Conference on Bioinformatics and Biomedicine, BIBM2012) Philadelphia PA USA, October 4, 2012, p. 241-247</ref>.
  5. Sentiment Extraction - We use an adaptation of the state-of-the-art sentiment extraction extraction technique developed by Chen et al<ref>Lu Chen, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang and Amit P. Sheth. Extracting Diverse Sentiment Expressions with Target-dependent Polarity from Twitter. In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM), 2012.</ref> to extraction on-target sentiment expressions from web forum data.
  6. Template Pattern Identification - We use a context-free grammar <ref>D. Cameron, A. P. Sheth, N. Jaykumar, G. Anand, K.Thirunarayan, G. A. Smith. A Hybrid Approach to Finding Relevant Social Media Content for Complex Domain Specific Information Needs Journal of Web Semantics. 29: 39-52. 2014. </ref> to define the query language of strings interpretable by PREDOSE. This is a necessary task since many of the complex information needs in PREDOSE require a knowledge of ontological concepts as well as concepts not defined in ontologies such as emotion, sentiment, intensity, frequency, dosage intervals etc.

Stage 3: Data Analysis & Interpretation

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.

  1. 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)
  2. 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.
  3. 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.
  4. 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.
  5. 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">R. Daniulaityte, R. Carlson, D. Cameron, G. A. Smith, A. P. Sheth, 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 CPDD 2014, San Juan, Puerto Rico, June 14-17, 2014</ref><ref name="dad-14"> R. Daniulaityte, R. Carlson, G. Brigham, D. Cameron, A. P. Sheth. "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. [PMC 4527156]</ref>

Loperamide-Withdrawal Discovery

In the early stages of the PREDOSE project we made a discovery, now reported in the literature<ref>R. Daniulaityte, R. Carlson, R. Falck, D. Cameron, S. Perera, L. Chen, A. P. Sheth. "I Just Wanted to Tell You That Loperamide WILL WORK": A Web-Based Study of Extra-Medical Use of Loperamide. Journal of Drug and Alcohol Dependence. 130(1-3): 241-244, 2013. ScienceDirect, [PMID 23201175]</ref> <ref>R. Daniulaityte, R. Carlson, R. Falck, D. Cameron, S. Perera, L. Chen, A. P. Sheth. A Web-Based Study of Self-Treatment of Opioid Withdrawal Symptoms with Loperamide. The College on Problems of Drug Dependence CPDD 2012, Palm Springs, CA USA, June 9-14, 2012.</ref>.

Based on the lexical and semantics-based techniques for entity identification various datasets were isolated according to drug mentions, based on mapping slang references to standard concepts. In one dataset, it was observed that users reported taking the anti-diarrhea treatment drug Loperamide (sold over the counter in Imodium) to self-medicate from withdrawal symptoms. The opioid addictions treatment drugs Buprenorphine and Methadone are commonly prescribed for treatment of withdrawal symptoms. Until now, it was unknown that Loperamide, can be (and is being) used for the same purpose. Which is more, it was observed that users reported the possibility of mild psychoactive (opiated) effects from megadosing - which is the practice of taking severely excessive amounts of a drug.

PREDOSE Live

http://knoesis-hpco.cs.wright.edu/predose/ [Video Demo]
http://knoesis-hpco.cs.wright.edu/knowledge-aware-search [Video Demo]

Publications

Related

  1. Researchers use social web forum data to understand nonmedical use of painkillers
  2. U.S. Targeting Prescription Drug Abuse
  3. Twitter Helps Determine "Morning People" and "Night Owls"
  4. Knowledge-Aware-Search

Related Projects

  1. Innovative NIDA National Early Warning Sysetm Network (iN3)
  2. EDrugTrends
  3. Hazards SEES: Social and Physical Sensing Enabled Decision Support
  4. DAO

Funding

This project was initially sponsored by the National Institutes of Health (NIH) Grant No. R21 DA030571-01A1 awarded to the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) and the Center for Interventions, Treatment and Addictions Research (CITAR) titled: A Study of Social Web Data on Buprenorphine Abuse using Semantic Web Technology. It is continued under National Institutes on Drug Abuse (NIDA) Grant No. R56DA038366-01, titled: NIDA National Early Warning System Network (iN3): An Innovative Approach (wiki page). Any opinions, findings, conclusions or recommendations expressed in this material are those of the investigator(s) and do not necessarily reflect the views of the National Institutes of Health.

Contact: Farahnaz Golroo

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