Mining interpretable and predictive diagnosis codes from multi-source electronic health records

Mining patterns from electronic health-care records (EHR) can potentially lead to better and more cost-effective treatments. We aim to find the groups of ICD-9 diagnosis codes from EHRs that can predict the improvement of urinary incontinence of home health care (HHC) patients and also are interpretable to domain experts. In this paper, we propose two approaches for increasing the interpretability of the obtained groups of ICD-9 codes. First, we incorporate prior information available from clinical domain knowledge using the clinical classification system (CCS). Second, we incorporate additional types of clinical information for the same patients, such as demographic, behavioral, physiological, and psycho-social variables available from survey questions during the hospital visits. Finally, we develop a hybrid framework that can combine both prior information and the datadriven clinical information in the predictive model framework. Our results obtained from a large-scale EHR data set show that the hybrid framework enhances clinical interpretability as compared to the baseline model obtained from ICD-9 codes only, while achieving almost the same predictive capability.