title: Personalized Predictive Modeling and Risk Factor Identification using Patient Similarity.
abstract: Personalized predictive models are customized for an individual patient and trained using information from similar patients. Compared to global models trained on all patients, they have the potential to produce more accurate risk scores and capture more relevant risk factors for individual patients. This paper presents an approach for building personalized predictive models and generating personalized risk factor profiles. A locally supervised metric learning (LSML) similarity measure is trained for diabetes onset and used to find clinically similar patients. Personalized risk profiles are created by analyzing the parameters of the trained personalized logistic regression models. A 15,000 patient data set, derived from electronic health records, is used to evaluate the approach. The predictive results show that the personalized models can outperform the global model. Cluster analysis of the risk profiles show groups of patients with similar risk factors, differences in the top risk factors for different groups of patients and differences between the individual and global risk factors.
authors: IBM T. J. Watson Research Center, Yorktown Heights, NY, USA.
authors: Kenney
authors: K
authors: Ng
authors: School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors: Jimeng
authors: J
authors: Sun
authors: IBM T. J. Watson Research Center, Yorktown Heights, NY, USA.
authors: Jianying
authors: J
authors: Hu
authors: IBM T. J. Watson Research Center, Yorktown Heights, NY, USA ; Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.
authors: Fei
authors: F
authors: Wang