Technical Approach : Time-series preprocessing and feature extraction

1. T. Bailey, Y. Chen, Y. Mao, C. Lu, G. Hackmann, S. T. Micek, K. Heard, K. Faulkner, and M. H. Kollef, “A Trial of a Real-time Alert for Clinical Deterioration in Patients Hospitalized on General Medical Wards.” Journal of Hospital Medicine, 8(5):236-242, 2013. http://www.cse.wustl.edu/~ychen/public/hospital.pdf Project Title: Integrated Real-Time Clinical Deterioration Prediction for Hospitalized Patients and Outpatients Motivation: • To develop integrated early warning system alerts for hospitalized patients employing both electronically available data and real-time vital signs data; • To establish predictive algorithms from informational cloud data for outpatients potentially at risk for hospital readmission; Transformative: • New algorithms for predicting clinical deterioration and readmission from noisy and high-dimensional data streams; • A novel alert explanation system which highlights the most relevant factors and suggests possible intervention; • An adaptive sensing reconfiguration scheme for better user experience and energy efficiency; Broader Impacts: • Improved clinical outcomes and potential reduction of both patient mortality rates and healthcare costs; • Widely-disseminated methods and software; • It addresses a major medical, societal, and governmental concern which has the potential to improve the overall administration of healthcare in the United States. Technical Approach: • Time-series preprocessing and feature extraction • Nonlinear, interpretable, scalable, and sparse time-series classification algorithms; • Data-driven dynamic sensing reconfiguration schemes;