TY - GEN
T1 - A data mining framework for the analysis of patient arrivals into healthcare centers
AU - Abdallah, Salam
AU - Malik, Mohsin
AU - Ertek, Gurdal
N1 - Funding Information:
This study was supported by Abu Dhabi Education Council (ADEC) under ADEC Award for Research Excellence (AARE 2015). The authors thank the public hospital in the United Arab Emirates (U.A.E.) for sharing their data for the research. The authors also thank Ayaz Salman for his help in the data cleaning and analysis process, as well as assisting in the writing of the paper.
Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/12/27
Y1 - 2017/12/27
N2 - 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.
AB - 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.
KW - Data mining
KW - Health informatics
KW - Healthcare information systems
KW - Patient arrival patterns
UR - http://www.scopus.com/inward/record.url?scp=85045521858&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045521858&partnerID=8YFLogxK
U2 - 10.1145/3176653.3176740
DO - 10.1145/3176653.3176740
M3 - Conference contribution
AN - SCOPUS:85045521858
T3 - ACM International Conference Proceeding Series
SP - 52
EP - 61
BT - Proceedings of the International Conference on Information Technology, ICIT 2017
PB - Association for Computing Machinery
T2 - 2017 International Conference on Information Technology, ICIT 2017
Y2 - 27 December 2017 through 29 December 2017
ER -