Natural Language Processing to Detect Cognitive Concerns in Electronic Health Records Using Deep Learning

TL;DR

This study demonstrates that deep learning-based NLP models can effectively identify patients with cognitive concerns from unstructured clinical notes, outperforming traditional structured data methods, thus aiding early detection of dementia.

Contribution

The paper introduces an attention-based deep learning NLP approach that surpasses baseline models using diagnosis codes and medication data for detecting cognitive issues in electronic health records.

Findings

Deep learning NLP model outperforms baseline methods.

Attention mechanism improves detection accuracy.

Unstructured clinician notes contain valuable diagnostic information.

Abstract

Dementia is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data. Information on cognitive dysfunction, however, is often found in unstructured clinician notes within medical records but manual review by experts is time consuming and often prone to errors. Automated mining of these notes presents a potential opportunity to label patients with cognitive concerns who could benefit from an evaluation or be referred to specialist care. In order to identify patients with cognitive concerns in electronic medical records, we applied natural language processing (NLP) algorithms and compared model performance to a baseline model that used structured diagnosis codes and medication data only. An attention-based deep learning model outperformed the baseline model and other simpler models.