NDLI: Ontology-based deep learning for human behavior prediction in health social networks

Content Provider	ACM Digital Library
Author	Kil, David Piniewski, Brigitte Phan, NhatHai Dou, Dejing Wang, Hao
Abstract	Human behavior prediction is a key component to studying the spread of wellness and healthy behavior in a social network. In this paper, we introduce an ontology-based Restricted Boltzmann Machine (ORBM) model for human behavior prediction in health social networks. We first propose a bottom-up algorithm to learn the user representation from ontologies. Then the user representation is used to incorporate self-motivation, social influences, and environmental events together in a human behavior prediction model, which extends a well-known deep learning method, Restricted Boltzmann Machines (RBMs), so that the interactions among the behavior determinants are naturally simulated through parameters. To our best knowledge, the ORBM model is the first ontology-based deep learning approach in health informatics for human behavior prediction. Experiments conducted on both real and synthetic data from health social networks have shown the tremendous effectiveness of our approach compared with conventional methods.
Starting Page	433
Ending Page	442
Page Count	10
File Format	PDF
ISBN	9781450338530
DOI	10.1145/2808719.2808764
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Deep learning Ontology Social network Health informatics
Content Type	Text
Resource Type	Article

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Ontology-based deep learning for human behavior prediction in health social networks