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Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records.

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Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records. International journal of medical informatics Wei, R., Buss, S. S., Milde, R., Fernandes, M., Sumsion, D., Davis, E., Kong, W. Y., Xiong, Y., Veltink, J., Rao, S., Westover, T. M., Petersen, L., Turley, N., Singh, A., Das, S., Junior, V. M., Ghanta, M., Gupta, A., Kim, J., Lam, A. D., Stone, K. L., Mignot, E., Hwang, D., Trotti, L. M., Clifford, G. D., Katwa, U., Thomas, R. J., Mukerji, S., Zafar, S. F., Westover, M. B., Sun, H. 2025; 200: 105917

Abstract

Unstructured and structured data in electronic health records (EHR) are a rich source of information for ÌÇÐÄ´«Ã½ and quality improvement studies. However, extracting accurate information from EHR is labor-intensive. Timely and accurate identification of patients with Alzheimer's Disease, related dementias (ADRD), or mild cognitive impairment (MCI) is critical for improving patient outcomes through early intervention, optimizing care plans, and reducing healthcare system burdens. Here we introduce an automated EHR phenotyping model to streamline this process and enable efficient identification of these conditions.We analyzed data from 3,626 outpatients seen at two hospitals between February 2015 and June 2022. Through manual chart review, we established ground truth labels for the presence or absence of MCI/ADRD diagnoses. Our model combined three types of data: (1) unstructured clinical notes, from which we extracted single words, two-word phrases (bigrams), and three-word phrases (trigrams) as features, weighted using Term Frequency-Inverse Document Frequency (TF-IDF) to capture their relative importance, (2) International Classification of Diseases (ICD) codes, and (3) medication prescriptions related to MCI/ADRD. We trained a regularized logistic regression model to predict MCI/ADRD diagnoses and evaluated its performance using standard metrics including area under the receiver operating curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, specificity, precision, recall, and F1 score.Thirty percent of patients in the cohort carried diagnoses of MCI/ADRD based on manual review. When evaluated on a held-out test set, the best model using clinical notes, ICDs, and medications, achieved an AUROC of 0.98, an AUPRC of 0.98, an accuracy of 0.93, a sensitivity (recall) of 0.91, a specificity of 0.96, a precision of 0.96, and an F1 score of 0.93 The estimated overall accuracy for patients randomly selected from EHRs was 99.88%.Automated EHR phenotyping accurately identifies patients with MCI/ADRD based on clinical notes, ICD codes, and medication records. This approach holds potential for large-scale MCI/ADRD ÌÇÐÄ´«Ã½ utilizing EHR databases.

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