ArticleProceedings of machine learning research2019
Clinical Judgement Study using Question Answering from Electronic Health Records.
Article in Proceedings of machine learning research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Question answering systems for health professionals at the point of care-a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Question Answering for Electronic Health Records: Scoping Review of Datasets and Models.Journal of medical Internet research · 2024Article
- Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study.BMC public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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 macroweighted 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.
Identifiers
31897452PMC6939641What 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.