Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation

We experiment with recent ensemble machine learning methods in estimating healthcare costs, utilizing Finnish data containing rich individual-level information on healthcare costs, socioeconomic status and diagnostic data from multiple registries. Our data are a random 10% sample (553,675 observatio...

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Veröffentlicht in:	The European journal of health economics 2024-09, Vol.25 (7), p.1117-1131
Hauptverfasser:	Holster, Tuukka, Ji, Shaoxiong, Marttinen, Pekka
Format:	Artikel
Sprache:	eng
Schlagworte:	Data analysis Economic Policy Health care Health care expenditures Health Care Management Health Economics Health services Machine learning Medicine Medicine & Public Health Original Paper Pharmacoeconomics and Health Outcomes Public Finance Public Health Regression analysis Socioeconomic factors Socioeconomics
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