Developing Knowledge-enhanced Chronic Disease Risk Prediction Models from Regional EHR Repositories
This addresses the localization issue in precision medicine for chronic disease risk prediction, offering a domain-specific incremental improvement.
The paper tackled the problem of poor performance when applying established chronic disease risk models to local populations by developing a knowledge-enhanced localized risk prediction model using regional EHR data and knowledge injection. The result was an improvement in AUC from 0.653 to 0.723, a 10.7% increase for ASCVD risk prediction in diabetes.
Precision medicine requires the precision disease risk prediction models. In literature, there have been a lot well-established (inter-)national risk models, but when applying them into the local population, the prediction performance becomes unsatisfactory. To address the localization issue, this paper exploits the way to develop knowledge-enhanced localized risk models. On the one hand, we tune models by learning from regional Electronic Health Record (EHR) repositories, and on the other hand, we propose knowledge injection into the EHR data learning process. For experiments, we leverage the Pooled Cohort Equations (PCE, as recommended in ACC/AHA guidelines to estimate the risk of ASCVD) to develop a localized ASCVD risk prediction model in diabetes. The experimental results show that, if directly using the PCE algorithm on our cohort, the AUC is only 0.653, while our knowledge-enhanced localized risk model can achieve higher prediction performance with AUC of 0.723 (improved by 10.7%).