Learning to predict health status of geriatric patients from observational data

Data for diagnosis and clinical studies are now typically gathered by hand. While more detailed, exhaustive behavioral assessments scales have been developed, they have the drawback of being too time consuming and manual assessment can be subjective. Besides, clinical knowledge is required for accurate manual assessment, for which extensive training is needed. Therefore our great research challenge is to leverage machine learning techniques to better understand patients health status automatically based on continuous computer observations. In this paper, we study the problem of health status prediction for geriatric patients using observational data. In the first part of this paper, we propose a distance metric learning algorithm to learn a Mahalanobis distance which is more precise for similarity measures. In the second part, we propose a robust classifier based on ℓ2,1-norm regression to predict the geriatric patients' health status. We test the algorithm on a dataset collected from a nursing home. Experiment shows that our algorithm achieves encouraging performance.

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