Mining Adverse Drug Reactions from online healthcare forums using Hidden Markov Model

BMC Medical Informatics and Decision Making

Research Article

Xue-wen Chen¹  Bo Luo²  Hariprasad Sampathkumar² 

[1] Dept. of Computer Science, Wayne State University, 48202, Detroit, USA;EECS, University of Kansas, 66045, Lawrence, USA;

关键词: Adverse drug reaction;    Pharmacovigilance;    Text mining;    Machine learning;    Online healthcare forums;    Hidden Markov model;   

DOI  :  10.1186/1472-6947-14-91

 received in 2013-12-24, accepted in 2014-08-18,  发布年份 2014

来源: Springer

【 摘 要 】

BackgroundAdverse Drug Reactions are one of the leading causes of injury or death among patients undergoing medical treatments. Not all Adverse Drug Reactions are identified before a drug is made available in the market. Current post-marketing drug surveillance methods, which are based purely on voluntary spontaneous reports, are unable to provide the early indications necessary to prevent the occurrence of such injuries or fatalities. The objective of this research is to extract reports of adverse drug side-effects from messages in online healthcare forums and use them as early indicators to assist in post-marketing drug surveillance.MethodsWe treat the task of extracting adverse side-effects of drugs from healthcare forum messages as a sequence labeling problem and present a Hidden Markov Model(HMM) based Text Mining system that can be used to classify a message as containing drug side-effect information and then extract the adverse side-effect mentions from it. A manually annotated dataset from http://www.medications.comis used in the training and validation of the HMM based Text Mining system.ResultsA 10-fold cross-validation on the manually annotated dataset yielded on average an F-Score of 0.76 from the HMM Classifier, in comparison to 0.575 from the Baseline classifier. Without the Plain Text Filter component as a part of the Text Processing module, the F-Score of the HMM Classifier was reduced to 0.378 on average, while absence of the HTML Filter component was found to have no impact. Reducing the Drug names dictionary size by half, on average reduced the F-Score of the HMM Classifier to 0.359, while a similar reduction to the side-effects dictionary yielded an F-Score of 0.651 on average. Adverse side-effects mined from http://www.medications.comand http://www.steadyhealth.comwere found to match the Adverse Drug Reactions on the Drug Package Labels of several drugs. In addition, some novel adverse side-effects, which can be potential Adverse Drug Reactions, were also identified.ConclusionsThe results from the HMM based Text Miner are encouraging to pursue further enhancements to this approach. The mined novel side-effects can act as early indicators for health authorities to help focus their efforts in post-marketing drug surveillance.

【 授权许可】

CC BY   
© Sampathkumar et al.; licensee BioMed Central Ltd. 2014