Moderator of 1 Session
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ACTIVE DEEP LEARNING TO DETECT COGNITIVE IMPAIRMENT IN ELECTRONIC HEALTH RECORDS
Abstract
Aims
Dementia is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data. Timely diagnosis of dementia, however, is important for patients and their caregivers to plan for the future. We aim to screen electronic health records (EHR) for signs of cognitive impairment.
Methods
To identify patients with cognitive impairment in EHR, we applied a deep learning based natural language processing (NLP) algorithm to unstructured clinician notes and compared the model’s performance to a baseline model that used regularized logistic regression with structured data (dementia related diagnosis codes and medication). We trained and evaluated the algorithm with a seed set of patients with detailed chart review and adjudication of cognitive status by a team of experts. Next, we used an active learning loop to continually improve model performance and select candidate cases for labeling using a combination of uncertainty and diversity measures.
Results
The baseline model with diagnosis codes and medications trained on the initial seed set (N=943) had an area under the receiver operating characteristic curve (AUROC) of 0.79. The deep learning model improved the AUROC to 0.92 and increased sensitivity of dementia detection from 0.59 to 0.79, at similar level of specificity. Of the patients which were predicted positive, 16.1% did not have a dementia-related diagnosis code suggesting that many patients with cognitive impairment may be undiagnosed.
Conclusions
Automatic processing of EHR with deep learning tools can be used to screen for patients with cognitive impairment who could benefit from a cognitive evaluation or be referred to specialist care.