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Precision medicine requires the precision disease risk prediction models. Inliterature, there have been a lot well-established (inter-)national riskmodels, but when applying them into the local population, the predictionperformance becomes unsatisfactory. To address the localization issue, thispaper exploits the way to develop knowledge-enhanced localized risk models. Onthe one hand, we tune models by learning from regional Electronic Health Record(EHR) repositories, and on the other hand, we propose knowledge injection intothe EHR data learning process. For experiments, we leverage the Pooled CohortEquations (PCE, as recommended in ACC/AHA guidelines to estimate the risk ofASCVD) to develop a localized ASCVD risk prediction model in diabetes. Theexperimental results show that, if directly using the PCE algorithm on ourcohort, the AUC is only 0.653, while our knowledge-enhanced localized riskmodel can achieve higher prediction performance with AUC of 0.723 (improved by10.7%).

Developing Knowledge-enhanced Chronic Disease Risk Prediction Models from Regional EHR Repositories