Naranjo Question Answering using End-to-End Multi-task Learning Model.

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ID: 71418
2019
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Abstract
In the clinical domain, it is important to understand whether an adverse drug reaction (ADR) is caused by a particular medication. Clinical judgement studies help judge the causal relation between a medication and its ADRs. In this study, we present the first attempt to automatically infer the causality between a drug and an ADR from electronic health records (EHRs) by answering the Naranjo questionnaire, the validated clinical question answering set used by domain experts for ADR causality assessment. Using physicians' annotation as the gold standard, our proposed joint model, which uses multi-task learning to predict the answers of a subset of the Naranjo questionnaire, significantly outperforms the baseline pipeline model with a good margin, achieving a macro-weighted f-score between 0.3652 - 0.5271 and micro-weighted f-score between 0.9523 - 0.9918.
Reference Key
rawat2019naranjokdd Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rawat, Bhanu Pratap Singh;Li, Fei;Yu, Hong;
Journal kdd : proceedings international conference on knowledge discovery & data mining
Year 2019
DOI
10.1145/3292500.3330770
URL
Keywords
lstm rnn attention based network multi-task learning naranjo questionnaire question answering

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