Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.

fall-detection

Fall Detection in EHR using Word Embeddings and Deep Learning

Author: Henrique D. P. dos Santos, Amanda P. Silva, Maria Carolina O. Maciel, Haline Maria V. Burin, Janete S. Urbanetto and Renata Vieira

Abstract: 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 health 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.

Keywords: Fall Detection, Electronic Health Records, Text Classification, Word Embeddings, Deep Learning

Full Text, BibText

Online Experiments

Run our experiments online with Binder

Binder

PUCRS A.I. in HealthCare

This project belongs to GIAS at PUCRS, Brazil

Releases

No releases published

Packages

No packages published

Languages