Similarity-based health risk prediction using Domain Fusion and electronic health records data

Electronic Health Record (EHR) data represents a valuable resource for individualized prospective prediction of health conditions. Statistical methods have been developed to measure patient similarity using EHR data, mostly using clinical attributes. Only a handful of recent methods have combined clinical analytics with other forms of similarity analytics, and no unified framework exists yet to measure comprehensive patient similarity. Here, we developed a generic framework named Patient similarity based on Domain Fusion (PsDF). PsDF performs patient similarity assessment on each available domain data separately, and then integrate the affinity information over various domains into a comprehensive similarity metric. We used the integrated patient similarity to support outcome prediction by assigning a risk score to each patient. With extensive simulations, we demonstrated that PsDF outperformed existing risk prediction methods including a random forest classifier, a regression-based model, and a naïve similarity method, especially when heterogeneous signals exist across different domains. Using PsDF and EHR data extracted from the data warehouse of Columbia University Irving Medical Center, we developed two different clinical prediction tools for two different clinical outcomes: incident cases of end stage kidney disease (ESKD) and severe aortic stenosis (AS) requiring valve replacement. We demonstrated that our new prediction method is scalable to large datasets, robust to random missingness, and generalizable to diverse clinical outcomes.