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Title: Fall Detection in Clinical Notes using Language Models and Token Classifier
Authors: Santos, Joaquim
Santos, Henrique
Vieira, Renata
Keywords: Language Models
Health Informatics
Issue Date: Jul-2020
Publisher: IEEE
Citation: J. Santos, H. D. P. dos Santos and R. Vieira, "Fall Detection in Clinical Notes using Language Models and Token Classifier," 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA, 2020, pp. 283-288, doi: 10.1109/CBMS49503.2020.00060.
Abstract: Electronic health records (EHR) are a key source of information to identify adverse events in patients. The largest category of adverse events in hospitals is fall incidents. The identification of such incidents guide to a better comprehension of the event and enhance the quality of patient health care. In this initial work, we compare the performance of SentenceClassifier (StC) against the Token-Classifier (TkC) with state-ofthe-art recurrent neural networks (RNN) to detect fall incidents in progress notes. Our experiments show that the use of deeplearning algorithms as token-classifier outperforms text-classifier. It improves fall identification using StC from 65% to 92% with TkC (F-Measure). Additionally, the token classifier is able to explain which words are most important in positive detection.
Type: article
Appears in Collections:CIDEHUS - Artigos em Livros de Actas/Proceedings

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