A classifying ubiquitous clinic recommendation approach for forming patient groups and recommending suitable clinics

Tin-Chih Chen*, Min Chi Chiu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Existing ubiquitous clinic recommendation systems assume that patients’ preferences are alike and thus apply the same recommendation mechanism to all mobile patients. However, this assumption may be unreasonable and must be relaxed. Accordingly, this study proposes a classifying ubiquitous recommendation approach. The classifying ubiquitous recommendation approach divides patients into multiple groups by mining their unknown preferences before recommending them suitable clinics. To tune the recommendation mechanism of each patient group, an integer nonlinear programming problem is solved in a rolling manner. When a new patient accesses the ubiquitous clinic recommendation system, the recommendation mechanisms of all groups are applied to recommend the suitable clinic to the new patient. The results of a regional experiment indicated that the classifying ubiquitous recommendation approach improved the successful recommendation rate by up to 52%. Therefore, patients’ unknown preferences are different and affect their behaviors in choosing clinics, which should be considered in grouping patients and in tailoring the clinic recommendation mechanism.

Original languageEnglish
Pages (from-to)165-174
Number of pages10
JournalComputers and Industrial Engineering
Volume133
DOIs
StatePublished - 1 Jul 2019

Keywords

  • Classifying
  • Clinic
  • Integer nonlinear programming
  • Ubiquitous recommendation

Fingerprint Dive into the research topics of 'A classifying ubiquitous clinic recommendation approach for forming patient groups and recommending suitable clinics'. Together they form a unique fingerprint.

Cite this