Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text

Database (Oxford). 2016 Jun 15;2016:baw094. doi: 10.1093/database/baw094. Print 2016.

Abstract

Drug toxicity is a major concern for both regulatory agencies and the pharmaceutical industry. In this context, text-mining methods for the identification of drug side effects from free text are key for the development of up-to-date knowledge sources on drug adverse reactions. We present a new system for identification of drug side effects from the literature that combines three approaches: machine learning, rule- and knowledge-based approaches. This system has been developed to address the Task 3.B of Biocreative V challenge (BC5) dealing with Chemical-induced Disease (CID) relations. The first two approaches focus on identifying relations at the sentence-level, while the knowledge-based approach is applied both at sentence and abstract levels. The machine learning method is based on the BeFree system using two corpora as training data: the annotated data provided by the CID task organizers and a new CID corpus developed by crowdsourcing. Different combinations of results from the three strategies were selected for each run of the challenge. In the final evaluation setting, the system achieved the highest Recall of the challenge (63%). By performing an error analysis, we identified the main causes of misclassifications and areas for improving of our system, and highlighted the need of consistent gold standard data sets for advancing the state of the art in text mining of drug side effects.Database URL: https://zenodo.org/record/29887?ln¼en#.VsL3yDLWR_V.

MeSH terms

  • Animals
  • Chemically-Induced Disorders* / genetics
  • Chemically-Induced Disorders* / metabolism
  • Crowdsourcing* / methods
  • Crowdsourcing* / standards
  • Data Mining / methods
  • Data Mining / standards
  • Databases, Factual / standards*
  • Humans
  • Machine Learning / standards*