Early illness recognition in older adults using transfer learning
Rayan Gargees, James M. Keller, Mihail Popescu · 2017
Predicting early signs of illness in older adults by utilizing a continuous, unobtrusive nursing home monitoring system has been shown to increase the quality of life and decrease the cost of care. Illness prediction is based on sensor data such as motion and bed and uses algorithms such as support vector machine (SVM) or k-nearest neighbor (kNN). One of the greatest challenges in developing prediction algorithms for sensor networks is utilizing knowledge from previous residents for predicting behavior in new ones. In this paper, we employ a transfer learning approach for addressing the cross resident training problem. We validate our method by conducting a retrospective study on three residents from TigerPlace, a retirement community in Columbia, MO, where apartments are fitted with wireless networks of motion and bed sensors. The ground truth, the daily presence or absence of the illness, was manually evaluated using nursing visit reports from an homegrown electronic medical record (EMR) system. In this study, transfer learning SVM approach outperformed other three methods, regular SVM, one class SVM, and one class kNN, resulting in average areas under the curve (AUCs) of 0.74, 0.52, 0.60 and 0.62 for three residents, respectively.