A hybrid model to identify fall occurrence from electronic health records.

ABSTRACT:

Introduction

Falls are a leading cause of unintentional injury in the elderly. Electronic health records (EHRs) offer the unique opportunity to develop models that can identify fall events. However, identifying fall events in clinical notes requires advanced natural language processing (NLP) to simultaneously address multiple issues because the word "fall" is a typical homonym.

Methods

We implemented a context-aware language model, Bidirectional Encoder Representations from Transformers (BERT) to identify falls from the EHR text and further fused the BERT model into a hybrid architecture coupled with post-hoc heuristic rules to enhance the performance. The models were evaluated on real world EHR data and were compared to conventional rule-based and deep learning models (CNN

SUBMITTER: Fu S 

PROVIDER: S-EPMC9448825 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature