Fall Detection in EHR using Word Embeddings and Deep Learning

Electronic health records (EHR) are an important source of information to detect adverse events in patients. In-hospital fall incidents represent the largest category of adverse event reports. The detection of such incidents leads to better understanding of the event and improves the quality of pati...

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Bibliographic Details
Main Authors: dos Santos, Henrique D.P., Silva, Amanda P., Maciel, Maria Carolina O., Burin, Haline Maria V., Urbanetto, Janete S., Vieira, Renata
Format: Conference Proceeding
Language:English
Subjects:
Biomedical Language Processing
Deep Learning
Electronic Health Records
Fall Detection
Word Embeddings
Online Access:Request full text
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Description
Summary:Electronic health records (EHR) are an important source of information to detect adverse events in patients. In-hospital fall incidents represent the largest category of adverse event reports. The detection of such incidents leads to better understanding of the event and improves the quality of patient health care. In this work, we evaluate several language models with state-of-the art recurrent neural networks (RNN) to detect fall incidents in progress notes. Our experiments show that the deep-learning approach outperforms previous works in the task of detecting fall events. Vector representation of words in the biomedical domain was able to detect falls with an F-Measure of 90%. Additionally, we made available an annotated dataset with 1,078 de-identified progress notes for replication purposes.
ISSN:2471-7819
DOI:10.1109/BIBE.2019.00054