ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Normalizing Adverse Events using Recurrent Neural Networks with Attention.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed, 3 citations in OpenAlex.
- LeafAI: query generator for clinical cohort discovery rivaling a human programmer.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adverse events (AEs) are undesirable outcomes of medication administration and cause many hospitalizations as well as even deaths per year. Information about AEs can enable their prevention. Natural language processing (NLP) techniques can identify AEs from narratives and match them to a structured terminology. We propose a novel neural network for AE normalization utilizing bidirectional long short-term memory (biLSTM) with attention mechanism that generalizes to diverse datasets. We train this network to first learn a framework for general AE normalization and then to learn the specifics of the task on individual corpora. Our results on the datasets from the Text Analysis Conference (TAC) 2017-ADR track, FDA adverse drug event evaluation shared task, and the Social Media Mining for Health Applications Workshop & Shared Task 2019 show that our approach outperforms widely used rule-based normalizers on a diverse set of narratives. Additionally, it outperforms the best normalization system by 4.86 in macro-averaged F1-score in the TAC 2017-ADR track.
Identifiers
32477654PMC7233057W3029278466What 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.