Natural Language Processing to Identify Patients with Cognitive Impairment

Abstract

Abstract INTRODUCTION Early detection of patients with cognitive impairment may facilitate care for individuals in this population. Natural language processing (NLP) is a potential approach to identifying patients with cognitive impairment from electronic health records (EHR). METHODS We used three machine learning algorithms (logistic regression, multilayer perceptron, and random forest) using clinical terms extracted by NLP to predict cognitive impairment in a cohort of 199 patients. Cognitive impairment was defined as a mini-mental status exams (MMSE) score <24. RESULTS NLP identified 69 (35%) patients with cognitive impairment and ICD codes identified 44 (22%). Using MMSE as a reference standard, NLP sensitivity was 35%, specificity 66%, precision 41%, and NPV 61%. The random forest method had the best test parameters; sensitivity 95%, specificity 100%, precision 100%, and NPV 97% DISCUSSION NLP can identify adults with cognitive impairment with moderate test performance that is enhanced with machine learning.