Linking predictive and prescriptive analytics of elderly and frail patient hospital services

Williams, Elizabeth, Gartner, Daniel ORCID: https://orcid.org/0000-0003-4361-8559 and Harper, Paul ORCID: https://orcid.org/0000-0001-7894-4907 2022. Linking predictive and prescriptive analytics of elderly and frail patient hospital services. Presented at: 10th IEEE International Conference on Healthcare Informatics (ICHI 2022), Rochester, MN, United States, 11-14 June 2022. 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI). p. 1. 10.1109/ICHI54592.2022.00071

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Abstract

Predictive and prescriptive techniques are being evaluated to predict demand for inpatient services within South East Wales. This work is specifically focusing on multi-site hospital services for elderly and frail patients, using classification and regression trees to determine patient clusters with similar attributes, yielding results of up to 89.62% accuracy. By incorporating these results into mathematical models we aim to quantify the value of incorporating the clustering results in a deterministic and stochastic mathematical programme. Index Terms—Machine Learning, Mathe

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Mathematics
Uncontrolled Keywords:	Machine Learning, Mathematical Programming, Stochastic Programming
Additional Information:	https://ohnlp.github.io/IEEEICHI2022/
Funders:	KESS2
Date of First Compliant Deposit:	13 May 2022
Date of Acceptance:	31 March 2022
Last Modified:	25 Nov 2022 15:15
URI:	https://orca.cardiff.ac.uk/id/eprint/149326

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