Natural Language Processing to Identify Patients with Cognitive Impairment

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.