Paper Info
Title
Mobile cloud-based depression diagnosis using an ontology and a Bayesian network.
Abstract
Recently, depression has becomes a widespread disease throughout the world. However, most people are not aware of the possibility of becoming depressed during their daily lives. Therefore, obtaining an accurate diagnosis of depression is an important issue in healthcare. In this study, we built an inference model based on an ontology and a Bayesian network to infer the possibility of becoming depressed, and we implemented a prototype using a mobile agent platform as a proof-of-concept in the mobile cloud. We developed an ontology model based on the terminology used to describe depression and we utilized a Bayesian network to infer the probability of becoming depressed. We also implemented the system using multi-agents to run on the Android platform, thereby demonstrating the feasibility of this method, and we addressed various implementation issues. The results showed that our method may be useful for inferring a diagnosis of depression.
Year
DOI
Venue
2015
10.1016/j.future.2014.05.004
Future Generation Computer Systems
Keywords
Field
DocType
Bayesian network,Depression diagnosis,Mobile and ubiquitous healthcare,Mobile cloud,Ontology application
Data mining,Ontology,Android (operating system),Terminology,Inference,Computer science,Mobile agent,Bayesian network,Mobile cloud,Cloud computing
Journal
Volume
Issue
ISSN
43
C
0167-739X
Citations 
PageRank 
References 
22
0.89
33
Authors
5
Name
Order
Citations
PageRank
Yue-Shan Chang129537.68
Chih-Tien Fan2417.02
Win-tsung Lo313911.94
Wan-Chun Hung4220.89
Shyan-Ming Yuan5634139.65