Fall Detection in Clinical Notes using Language Models and Token Classifier

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.

I. Introduction

With the growth of Artificial Intelligence, predictions based on Electronic Health Records (EHRs) are becoming increasingly viable. As EHRs consolidate information from all patient timelines, they can be processed as input for machine learning models to build Clinical Decision Support Systems (CDSS), in order to aid the hospital workflow. Several studies show the potential of using EHR data for comorbidity index prediction [1], bringing smart screening systems to pharmacy services [2] along with many other possibilities [3].