Using Google Trends to Predict Pediatric Respiratory Syncytial Virus Encounters at a Major Health Care System To assess whether Google search activity predicts leadtime for pediatric respiratory syncytial virus (RSV) encounters within a major health care system. Internet user search and health system encounter database analysis. Pediatric RSV encounter volumes across all clinics and hospitals in the Duke Health system were tabulated from 2005 to 2016. North Carolina Google user search activity for RSV were obtained over the same time period. Time series analysis was used to compare RSV encounters and search activity. Crosscorrelation was used to determine the xe2x80x98lagxe2x80x99 time difference between Google user search interest for RSV and observed Pediatric RSV encounter volumes. Google search activity and Pediatric RSV encounter volumes demonstrated strong seasonality with predilection for winter months. Granger Causality testing revealed that North Carolina RSV Google search activity can predict pediatric RSV encounters at our health system (Fxe2x80x89xe2x80x895.72, pxe2x80x89u003cxe2x80x890.0001). Using crosscorrelation, increases in Google search activity provided lead time of 0.21xc2xa0weeks (1.47xc2xa0days) prior to observed increases in Pediatric RSV encounter volumes at our health system. RSV is a common cause of upper airway obstruction in pediatric patients for which pediatric otolaryngologists are consulted. We demonstrate that Google search activity can predict RSV patient interactions with a major health system with a measurable leadtime. The ability to predict when illnesses in a population result in increased health care utilization would be an asset to health system providers, planners and administrators. Prediction of RSV would allow specific care pathways to be developed and resource needs to be anticipated before actual presentation.

Supervised Mixture Models for Population Health We examine a machine learning approach for deriving insights from observational healthcare data in order to improve public health. Our goal is to simultaneously identify patient subpopulations with differing health risks and find the distinct risk factors or determinants associated with each subpopulation. Here, we develop a supervised Gaussian Mixture Model (GMM) approach for subpopulation modeling that combines GMMs with L1logistic regression. We demonstrate the approach on an analysis of high cost drivers of Medicaid expenditures for inpatient stays associated with Newborn, Pregnancy, and Circulatory Systems diagnostic categories. These conditions were chosen because they had the highest total inpatient expenditures in New York State (NYS) in 2016. When compared with stateoftheart learning methods (random forests, boosting, deep learning), our approach provides comparable prediction performance but also extracts insightful explanations of the subpopulation structure and risk factors within each subpopulation. Sequentially applying unsupervised learning methods and then applying logistic regression fails to yield equally meaningful results: the unsupervised subpopulations are homogeneous and moderately predictable, while some of our subpopulations are highly predictable with easytoidentify drivers of cost. Focusing on newborns, we unveil subpopulations indicative of the landscape of healthcare in NYS: about 90% of the discharges are healthy New York City babies and about 1% are costly complex cases. Subpopulations indicate regional disparities: for example newborns from Central, Southern and Western NY are of higher risk for highcost stays associated with substance abuse. The results indicate the promise of the approach for future population health studies based on electronic health care records.