Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.

Journal of the American Medical Informatics Association : JAMIA
Fenia ChristopoulouSophia Ananiadou

Abstract

Identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related entities in electronic health records. We proposed an ensemble approach for relation extraction and classification between drugs and medication-related entities. We incorporated state-of-the-art named-entity recognition (NER) models based on bidirectional long short-term memory (BiLSTM) networks and conditional random fields (CRF) for end-to-end extraction. We additionally developed separate models for intra- and inter-sentence relation extraction and combined them using an ensemble method. The intra-sentence models rely on bidirectional long short-term memory networks and attention mechanisms and are able to capture dependencies between multiple related pairs in the same sentence. For the inter-sentence relations, we adopted a neural architecture that utilizes the Transformer network to improve performance in longer sequences. Our team ranked third with a micro-averaged F1 score of 94.72% and 87.65% for relation and end-to-end relation extracti...Continue Reading

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Citations

Oct 5, 2019·Journal of the American Medical Informatics Association : JAMIA·Sam HenryOzlem Uzuner
Aug 22, 2020·Yearbook of Medical Informatics·Udo Hahn, Michel Oleynik
Oct 23, 2020·BMJ Open·Eva N HamulyákArmand M Leroi
May 6, 2021·JMIR Medical Informatics·Ghada AlfattniGoran Nenadic
Nov 13, 2021·Drug Safety : an International Journal of Medical Toxicology and Drug Experience·Seunghee LeeSuehyun Lee

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Software Mentioned

Transformer
DEIDTERM
OSCAR4
LingPipe

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