Side effect PTM: An unsupervised topic model to mine adverse drug reactions from health forums

Sheng Wang, Yanen Li, Duncan Ferguson, Chengxiang Zhai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automatic discovery of medical knowledge using data mining has great potential benefit in improving population health and reducing healthcare cost. Discovering adverse drug reaction (ADR) is especially important because of the significant morbidity of ADRs to patients. Recently, more and more patients describe the ADRs they experienced and seek for help through online health forums, creating great opportunities for these forums to discover previously unknown ADRs. In this paper, we propose a novel unsupervised approach to tap into the increasingly available health forums to mine the side effect symptoms of drugs mentioned by forum users. Our approach is based on a novel probabilistic mixture model of symptoms, where the side effect symptoms and disease symptoms are explicitly modeled with two separate component models, and discovery of side effect symptoms can be achieved in an unsupervised way through fit- Ting the mixture model to the forum data. Extensive experiments on online health forums demonstrate that our proposed model is effective for discovering the reported ADRs on forums in a completely unsupervised way. The mined knowledge using our model is directly useful for increasing our understanding of more challenging ADRs, such as long-term side effects, drug-drug interactions, and rare side effects. Since our approach is unsupervised, it can be applied to mining large amounts of growing forum data to discover new knowledge about ADRs, helping many patients become aware of possible ADRs.

Original languageEnglish (US)
Title of host publicationACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages321-330
Number of pages10
ISBN (Electronic)9781450328944
DOIs
StatePublished - Sep 20 2014
Event5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM BCB 2014 - Newport Beach, United States
Duration: Sep 20 2014Sep 23 2014

Publication series

NameACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Other

Other5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM BCB 2014
CountryUnited States
CityNewport Beach
Period9/20/149/23/14

Keywords

  • Adverse drug reaction
  • Health forum
  • Probabilistic topic model

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Software
  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Side effect PTM: An unsupervised topic model to mine adverse drug reactions from health forums'. Together they form a unique fingerprint.

Cite this