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Using Machine Learning to Improve Personalised Prediction: A Data-Driven Approach to Segment and Stratify Populations for Healthcare
Will Yuill, Holger Kunz
29 - 32
10.3233/SHTI210851
Research Article
Studies in Health Technology and Informatics
Volume 289: Informatics and Technology in Clinical Care and Public Health
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.

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