SHB: Small: Computational Algorithms for Predictive Health Assessment

University Of Missouri-Columbia, Columbia MO

Investigators

Abstract

Abstract: SHB: Small: Computational Algorithms for Predictive Health Assessment This project leverages ongoing work at Tiger Place (TP), University of Missouri (MU) in the use of sensor technology for in-home health assessment. The TP team has deployed sensor networks in the homes of seniors, with a wide range of sensor types and analysis approaches. They are integrating their sensor networks with an in-house nursing electronic health record (EHR) and investigating health context-aware computational algorithms for health and wellbeing assessments. The proposed project has the following objectives: (1) Integrate the sensor network with an EHR developed in-house to provide automatic health context for comprehensive algorithm development; (2) Investigate algorithms for identifying health patterns based on sensor data and contextual health information such as chronic conditions and medication changes that are provided by the EHR data; and (3) Investigate the possibility of predicting physiological changes such as blood pressure based on sensor data. A variety of machine learning methods are investigated for predictive health assessment. There are two potential difficulties that this research tackles: ground truth uncertainty and data unbalance. To address these problems the project is developing two new machine learning methods: a fuzzy extension of multiple instance learning and a sensor firing sequence similarity based method for recognizing pattern changes. Existing sensor data is used as a starting point to develop the proposed methods, along with simulated data for more diverse testing scenarios. The integrated combination of the sensor network and EHR is expected provide a unique, rich dataset in which to investigate health context-aware algorithms.