Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records

TL;DR

This paper presents a deep learning NLP approach that effectively identifies patients with cognitive impairment from electronic health records, surpassing traditional methods and capturing undiagnosed cases.

Contribution

It introduces an attention-based deep learning model that leverages linguistic context to improve detection of cognitive impairment in EHRs, including cases without explicit ICD codes.

Findings

Deep learning model achieved 0.93 accuracy.

Linguistic context improves classification performance.

Successfully identified undiagnosed dementia patients.

Abstract

Dementia is a neurodegenerative disorder that causes cognitive decline and affects more than 50 million people worldwide. Dementia is under-diagnosed by healthcare professionals - only one in four people who suffer from dementia are diagnosed. Even when a diagnosis is made, it may not be entered as a structured International Classification of Diseases (ICD) diagnosis code in a patient's charts. Information relevant to cognitive impairment (CI) is often found within electronic health records (EHR), but manual review of clinician notes by experts is both time consuming and often prone to errors. Automated mining of these notes presents an opportunity to label patients with cognitive impairment in EHR data. We developed natural language processing (NLP) tools to identify patients with cognitive impairment and demonstrate that linguistic context enhances performance for the cognitive impairment classification task. We fine-tuned our attention based deep learning model, which can learn from complex language structures, and substantially improved accuracy (0.93) relative to a baseline NLP model (0.84). Further, we show that deep learning NLP can successfully identify dementia patients without dementia-related ICD codes or medications.