Yuill, W;
Kunz, H;
(2022)
Using Machine Learning to Improve Personalised Prediction: A Data-Driven Approach to Segment and Stratify Populations for Healthcare.
Studies in Health Technology and Informatics
, 289
pp. 29-32.
10.3233/SHTI210851.
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Abstract
Population Health Management typically relies on subjective decisions to segment and stratify populations. This study combines unsupervised clustering for segmentation and supervised classification, personalised to clusters, for stratification. An increase in cluster homogeneity, sensitivity and positive predictive value was observed compared to an unlinked approach. This analysis demonstrates the potential for a cluster-then-predict methodology to improve and personalise decisions in healthcare systems.
| Type: | Article |
|---|---|
| Title: | Using Machine Learning to Improve Personalised Prediction: A Data-Driven Approach to Segment and Stratify Populations for Healthcare |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3233/SHTI210851 |
| Publisher version: | https://doi.org/10.3233/SHTI210851 |
| Language: | English |
| Additional information: | © 2022 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (https://creativecommons.org/licenses/by-nc/4.0/deed.en_US). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10142109 |
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