Mobile cloud-based depression diagnosis using an ontology and a Bayesian network

Yue Shan Chang*, Chih Tien Fan, Win Tsung Lo, Wan Chun Hung, Shyan-Ming Yuan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


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.

Original languageEnglish
Pages (from-to)87-98
Number of pages12
JournalFuture Generation Computer Systems
StatePublished - Feb 2015


  • Bayesian network
  • Depression diagnosis
  • Mobile and ubiquitous healthcare
  • Mobile cloud
  • Ontology application

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