title: Mining adverse drug reactions from online healthcare forums using hidden Markov model.
abstract: BACKGROUND
  Adverse 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.
  
  
  METHODS
  We 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.com is used in the training and validation of the HMM based Text Mining system.
  
  
  RESULTS
  A 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.com and http://www.steadyhealth.com were 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.
  
  
  CONCLUSIONS
  The 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.
authors.affiliation: EECS, University of Kansas, 66045 Lawrence, USA. hariprsd@ku.edu.
authors.forename: Hariprasad
authors.initials: H
authors.lastname: Sampathkumar
authors.forename: Xue-wen
authors.initials: XW
authors.lastname: Chen
authors.forename: Bo
authors.initials: B
authors.lastname: Luo
mesh_terms: D057225:Data Mining / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D020407:Internet; D008390:Markov Chains; D060756:Social Networking