A data mining framework for the analysis of patient arrivals into healthcare centers

Salam Abdallah, Mohsin Malik, Gurdal Ertek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We present a data mining framework that can be applied for analyzing patient arrivals into healthcare centers. The sequentially applied methods are association mining, text cloud analysis, Pareto analysis, cross-tabular analysis, and regression analysis. We applied our framework using real-world data from a one of the largest public hospitals in the U.A.E., demonstrating its applicability and possible benefits. The dataset used was eventually 110,608 rows in total for the regression models, covering the most utilized 14 hospital units. The dataset is at least 10-fold larger than datasets used in closely-related research. The developed data mining framework can provide the input for a subsequent optimization model, which can be used to optimally assign appointments for patients, based on their arrival patterns.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Information Technology, ICIT 2017
PublisherAssociation for Computing Machinery
Pages52-61
Number of pages10
ISBN (Electronic)9781450363518
DOIs
Publication statusPublished - Dec 27 2017
Externally publishedYes
Event2017 International Conference on Information Technology, ICIT 2017 - Singapore, Singapore
Duration: Dec 27 2017Dec 29 2017

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2017 International Conference on Information Technology, ICIT 2017
Country/TerritorySingapore
CitySingapore
Period12/27/1712/29/17

Keywords

  • Data mining
  • Health informatics
  • Healthcare information systems
  • Patient arrival patterns

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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