1 / 12

PREDOSE: PRE scription D rug abuse O nline- S urveillance and E pidemiology

PREDOSE: PRE scription D rug abuse O nline- S urveillance and E pidemiology. Delroy Cameron 9/29/2011. GOAL. To determine user knowledge , attitudes and behavior related to the non-medical use of pharmaceutical opioids as discussed on Web-based forums

orsen
Download Presentation

PREDOSE: PRE scription D rug abuse O nline- S urveillance and E pidemiology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. PREDOSE:PREscriptionDrug abuse Online-Surveillance and Epidemiology Delroy Cameron 9/29/2011

  2. GOAL • To determine user knowledge, attitudes and behavior related to the non-medical use of pharmaceutical opioids as discussed on Web-based forums • To determine temporal trends and patterns in pharmaceutical opioid abuse as discussed on Web-based forums

  3. Problem • Scalability • Data Collection • Flexibility • Data Annotation • Latency of Relevant Information • Reactive Effect

  4. Aspects • Information Extraction • Entity Identification/Disambiguation • Relationship Extraction • Triple Extraction • Trend Detection • Spatio-Temporal-Thematic Analysis • Sentiment Analysis

  5. Stage 3. Data Analysis and Interpretation Qualitative and Quantitative Analysis of Drug User Knowledge, Attitudes and Behaviors Cuebee Scooner 9 10 Semantic Web Tools Temporal Analysis for Trend Detection Stage 2. Automatic Coding Semantic Web Database Ontology Information Extraction Module 8 6 Schema Machine Learning Natural Language Processing 5 e.g. Opioid, Pain Pills = + Instances e.g. Suboxone, Subutex Named Entity Identification, Relationship Extraction 7 Triples/RDF Database Stage 1. Data Collection 3 1 2 Web Crawler 4 Web Forums Data Cleaning Informal Text Database

  6. Entity Disambiguation • Semantic Similarity Oxycontin OP “Experiment 2: Fail - Grinding up and parachuting  - despite milling these OPs down, they still retain substantial time release. I found this to be a failure and it released the oxy slowly over the course of many hours. bad boys Oxycontin OP Oxycontin OC Oxycontin Candidates Context oxy oxy Oxycontin OC Oxycontin OP oxy oxy Oxycontin

  7. Probabilistic Entity Disambiguation “Experiment 2: Fail - Grinding up and parachuting  - despite milling these OPs down, they still retain substantial time release. I found this to be a failure and it released the oxy slowly over the course of many hours .”

  8. Entity Identification/Disambiguation Architecture Drugbank Erowid DrugSlang NDCP NIDA Vocabulary Known Slang/Drug References Opiophile.com Informal Text Database Bluelight.ru DrugsandBooze.com Vocabulary Corpus/Subset Language Model Doc

  9. Sentiment Analysis • Entities • Oxy, OP • Sentiment Clues • failure, despite • Polarity “Experiment 2: Fail - Grinding up and parachuting  - despite milling these OPs down, they still retain substantial time release. I found this to be a failure and it released the oxy slowly over the course of many hours.

  10. Temporal Analysis (TIMELIME)

  11. People • kno.e.sis • Delroy Cameron • SujanUdayanga • Amit P. Sheth • CITAR (Center for Interventions, Treatment and Addictions Research) • Dr. Raminta Daniulaityte • Dr. Robert Carlson • RusselFalck

  12. Questions http://wiki.knoesis.org/index.php/PREDOSE

More Related