Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records
This addresses the challenge of automated and accurate identification of cognitive impairment in healthcare data, which is incremental as it builds on existing NLP methods with specific improvements.
The researchers tackled the problem of under-diagnosis of cognitive impairment in electronic health records by developing a deep learning NLP model that identifies patients with cognitive impairment, achieving an accuracy of 0.93 compared to a baseline of 0.84 and successfully detecting dementia patients without structured codes or medications.
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.