ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2019
Naranjo Question Answering using End-to-End Multi-task Learning Model.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- ScAN: Suicide Attempt and Ideation Events Dataset.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2022Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2020Article
- Neural Multi-Task Learning for Adverse Drug Reaction Extraction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.