Application of data mining in engineering project management of the health sector

被引：0

作者：

Liu S. ^{[1
]}

Gao Y. ^{[1
]}

Sitiaida S. ^{[1
]}

机构：

[1] LimKokWing University, Selangor, Cyberjaya

来源：

Journal of Commercial Biotechnology | 2022年 / 27卷 / 04期

关键词：

Application analysis; Commercial bioengineering; Data mining; Engineering project management;

D O I：

10.5912/jcb1414

中图分类号：

学科分类号：

摘要：

Engineering project management is an essential component of the healthcare sector, as it involves the planning, execution, and monitoring of projects related to medical technology, infrastructure, and other areas of healthcare. The application of data mining techniques in engineering project management has the potential to improve project planning and execution, enhance decision-making, and optimize resource allocation. Data mining involves the extraction of patterns and insights from large data sets, which can be used to identify trends, relationships, and patterns that may not be immediately apparent through manual analysis. In the healthcare sector, data mining can be applied to various areas of engineering project management, including project planning, risk assessment, resource allocation, and progress monitoring. By applying data mining techniques to engineering project management in the healthcare sector, project managers can gain a more comprehensive understanding of project risks, resource needs, and progress. This can enable them to make more informed decisions and allocate resources more efficiently. In addition, data mining can be used to identify areas of inefficiency or bottlenecks in the project management process, enabling managers to optimize processes and improve project outcomes. © 2022 ThinkBiotech LLC. All rights reserved.

引用

页码：109 / 119

页数：10

共 20 条

[1]

Kammoun M. A., Hajej Z., Rezg N., A multi-level selective maintenance strategy combined to data mining approach for multicomponent system subject to propagated failures, Journal of Systems Science and Systems Engineering, 31, 3, pp. 313-337, (2022)

[2]

Van Dyke M., Klemetti T., Wickline J., Geologic data collection and assessment techniques in coal mining for ground control, International journal of mining science and technology, 30, 1, pp. 131-139, (2020)

[3]

Eskandari M., Alizadeh Bahmani A. H., Mardani-Fard H. A., Karimzadeh I., Omidifar N., Peymani P., Evaluation of factors that influenced the length of hospital stay using data mining techniques, BMC Medical Informatics and Decision Making, 22, 1, pp. 1-11, (2022)

[4]

Yurum O. R., Taskaya-Temizel T., Yildirim S., The use of video clickstream data to predict university students’ test performance: A comprehensive educational data mining approach, Education and Information Technologies, pp. 1-32, (2022)

[5]

Carrasquilla A., Tavares M., Franco Filho N., Using facies, data mining and artificial intelligence concepts in the evaluation of a carbonate reservoir in Campos basin, Southeastern Brazil, Journal of South American Earth Sciences, 120, (2022)

[6]

Hamdi A., Shaban K., Erradi A., Mohamed A., Rumi S. K., Salim F. D., Spatiotemporal data mining: a survey on challenges and open problems, Artificial Intelligence Review, 55, 2, pp. 1441-1488, (2022)

[7]

Zhang L., Chen H., Zheng M., Research on risk assessment method of energy system based on data mining, International Journal of Global Energy Issues, 44, 1, pp. 47-64, (2022)

[8]

Amirtash P., Parchami Jalal M., Jelodar M. B., Integration of project management services for International Engineering, Procurement and Construction projects, Built Environment Project and Asset Management, 11, 2, pp. 330-349, (2021)

[9]

Mohammed O. A., Vafaei S., Kurdalivand M. M., Rasooli S., Yao C., Hu T., A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran, Sustainability, 14, 20, (2022)

[10]

Mandreoli F., Ferrari D., Guidetti V., Motta F., Missier P., Real-world data mining meets clinical practice: Research challenges and perspective, Frontiers in big Data, (2022)

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