Clinical Judgement Study Using Question Answering from Electronic Health Records
Abstract
Clinical judgement studies are essential for recognising the causal relation of a medication with adverse drug reactions (ADRs). Traditionally, these studies are conducted via expert manual chart review. By contrast, we propose an end-to-end deep learning question answering model to automatically infer such causal relations. Our proposed model identifies the causal relation by answering a subset of Naranjo questionnaire Naranjo et al. (1981) from electronic health records. It employs multi-level attention layers along with local and global context while answering these questions. Our proposed model achieves a macro-weighted F-score of 0.4598 - 0.5142 across the selected questions and an overall F-score of 0.5011. We also did an ablation study to validate the importance of local and global context for the model.
Cite
Text
Rawat et al. "Clinical Judgement Study Using Question Answering from Electronic Health Records." Proceedings of the 4th Machine Learning for Healthcare Conference, 2019.Markdown
[Rawat et al. "Clinical Judgement Study Using Question Answering from Electronic Health Records." Proceedings of the 4th Machine Learning for Healthcare Conference, 2019.](https://mlanthology.org/mlhc/2019/rawat2019mlhc-clinical/)BibTeX
@inproceedings{rawat2019mlhc-clinical,
title = {{Clinical Judgement Study Using Question Answering from Electronic Health Records}},
author = {Rawat, Bhanu Pratap Singh and Li, Fe and Yu, Hong},
booktitle = {Proceedings of the 4th Machine Learning for Healthcare Conference},
year = {2019},
pages = {216-229},
volume = {106},
url = {https://mlanthology.org/mlhc/2019/rawat2019mlhc-clinical/}
}