Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup

Abstract

Over 78 million people will suffer from dementia by 2030, emphasizing the need for early identification of patients with mild cognitive impairment (MCI) at risk, and personalized clinical evaluation steps to diagnose potentially reversible causes. Here, we leverage real-world electronic health records in the observational medical outcomes partnership (OMOP) data model to develop machine learning models to predict MCI up to a year in advance of recorded diagnosis. Our experimental results with logistic regression, random forest, and xgboost models trained and evaluated on more than 531K patient visits show random forest model can predict MCI onset with ROC-AUC of 68.2±0.7. We identify the clinical factors mentioned in clinician notes that are most predictive of MCI. Using similar association mining techniques, we develop a data-driven list of clinical procedures commonly ordered in the workup of MCI cases, that could be used as a basis for guidelines and clinical order set templates.

Figure 1.

Predictors extracted from structured and unstructured data of an example patient. The models use these clinical factors in patient’s historical data on or prior their primary care visit to predict MCI diagnosis within one year of the primary care visit.

Figure 2.

Top 30 Clinical factors identified by random forest model that provide increased power in discriminating MCI and Non-MCI samples.