An intelligent recommender system based on predictive analysis in telehealthcare environment

Raid Lafta*, Ji Zhang, Xiaohui Tao, Yan Li, Vincent Shin-Mu Tseng, Yonglong Luo, Fulong Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

The use of intelligent technologies for providing useful recommendations to patients suffering chronic diseases may play a positive role in improving the general life quality of patients and help reduce the workload and cost involved in their daily healthcare. The objective of this study is to develop an intelligent recommender system based on predictive analysis for advising patients in the telehealth environment concerning whether they need to take the body test one day in advance by analyzing medical measurements of a patient for the past k days. The proposed algorithms supporting the recommender system have been validated using a time series telehealth data recorded from heart disease patients which were collected from May to January 2012, from our industry collaborator Tunstall. The experimental results show that the proposed system yields satisfactory recommendation accuracy and offer a promising way for saving the workload for patients to conduct body tests every day. This study highlights the possible usefulness of the computerized analysis of time series telehealth data in providing appropriate recommendations to patients suffering chronic diseases such as heart diseases patients.

Original languageEnglish
Pages (from-to)325-336
Number of pages12
JournalWeb Intelligence
Volume14
Issue number4
DOIs
StatePublished - 1 Jan 2016

Keywords

  • Intelligent system
  • heart failure
  • recommender system
  • telehealth
  • time series prediction

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