TY - JOUR
T1 - A deep learning approach for human behavior prediction with explanations in health social networks
T2 - social restricted Boltzmann machine (SRBM+)
AU - Phan, Nhathai
AU - Dou, Dejing
AU - Piniewski, Brigitte
AU - Kil, David
N1 - Funding Information:
This work is supported by the NIH Grant R01GM103309 to the SMASH project. We thank Xiao Xiao, Rebeca Sacks, and Ellen Klowden for their contributions. Dr. Phan currently is an Assistant Professor at New Jersey Institute of Technology. The work was done when he was a Research Associate at the University of Oregon.
Publisher Copyright:
© 2016, Springer-Verlag Wien.
PY - 2016/12/1
Y1 - 2016/12/1
N2 - Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems will be actually adopted, thus driving engagement and loyalty. However, most prediction models do not provide explanations for the behaviors they predict. In this paper, we study the research problem, human behavior prediction with explanations, for healthcare intervention systems in health social networks. In this work, we propose a deep learning model, named social restricted Boltzmann machine (SRBM), for human behavior modeling over undirected and nodes-attributed graphs. In the proposed SRBM+ model, we naturally incorporate self-motivation, implicit and explicit social influences, and environmental events together. Our model not only predicts human behaviors accurately, but also, for each predicted behavior, it generates explanations. Experimental results on real-world and synthetic health social networks confirm the accuracy of SRBM+ in human behavior prediction and its quality in human behavior explanation.
AB - Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems will be actually adopted, thus driving engagement and loyalty. However, most prediction models do not provide explanations for the behaviors they predict. In this paper, we study the research problem, human behavior prediction with explanations, for healthcare intervention systems in health social networks. In this work, we propose a deep learning model, named social restricted Boltzmann machine (SRBM), for human behavior modeling over undirected and nodes-attributed graphs. In the proposed SRBM+ model, we naturally incorporate self-motivation, implicit and explicit social influences, and environmental events together. Our model not only predicts human behaviors accurately, but also, for each predicted behavior, it generates explanations. Experimental results on real-world and synthetic health social networks confirm the accuracy of SRBM+ in human behavior prediction and its quality in human behavior explanation.
KW - Deep learning
KW - Explanation
KW - Health social network
KW - Human behavior
KW - Prediction
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U2 - 10.1007/s13278-016-0379-0
DO - 10.1007/s13278-016-0379-0
M3 - Article
AN - SCOPUS:84987736338
SN - 1869-5450
VL - 6
JO - Social Network Analysis and Mining
JF - Social Network Analysis and Mining
IS - 1
M1 - 79
ER -