Amodular approach to integrating multiple data sources 1 into real time clinical prediction for pediatric diarrhea 2
Traditional clinical prediction models focus on parameters of the individual patient. For 22 infectious diseases, sources external to the patient, including characteristics of prior patients and 23 seasonal factors, may improve predictive performance. We describe the development of a 24 predictive model that integrates multiple sources of data in a principled statistical framework using 25 a post test odds formulation. Our method enables electronic real time updating and flexibility, 26 such that components can be included or excluded according to data availability. We apply this 27 method to the prediction of etiology of pediatric diarrhea, where "pre test" epidemiologic data may 28 be highly informative. Diarrhea has a high burden in low resource settings, and antibiotics are 29 often over prescribed. We demonstrate that our integrative method outperforms traditional 30 prediction in accurately identifying cases with a viral etiology, and show that its clinical application, 31 especially when used with an additional diagnostic test, could result in a 61% reduction in 32 inappropriately prescribed antibiotics. 33