ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2022
Machine learning versus regression modelling in predicting individual healthcare costs from a representative sample of the nationwide claims database in France.
Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Development of Machine Learning Models to Predict Health Insurance Claim Costs Among Older Indonesians: A Retrospective Predictive Modeling Study.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2026Article
- Risk Governance of Payment Exceptions Under China's DRG/DIP Reform: A Structured Policy-Text Analysis of Special Case Negotiation Rules.Risk management and healthcare policy · 2026Article
- Impact of multimorbidity on healthcare costs in patients with type 2 diabetes in China: a longitudinal analysis of health insurance claims data.Archives of public health = Archives belges de sante publique · 2025Article
- Comparative models on low multiplier DRG classification for advanced lung cancer.Frontiers in public health · 2025Article
- Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation.The European journal of health economics : HEPAC : health economics in prevention and care · 2024Article
- Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities.Frontiers in drug safety and regulation · 2024Review
- ChatGPT's quiz skills in different otolaryngology subspecialties: an analysis of 2576 single-choice and multiple-choice board certification preparation questions.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2023Article
- Using machine-learning algorithms to improve imputation in the medical expenditure panel survey.Health services research · 2023Article
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3 authors.
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Abstract
backgroundInnovative provider payment methods that avoid adverse selection and reward performance require accurate prediction of healthcare costs based on individual risk adjustment. Our objective was to compare the performances of a simple neural network (NN) and random forest (RF) to a generalized linear model (GLM) for the prediction of medical cost at the individual level.
methodsA 1/97 representative sample of the French National Health Data Information System was used. Predictors selected were: demographic information; pre-existing conditions, Charlson comorbidity index; healthcare service use and costs. Predictive performances of each model were compared through individual-level (adjusted R-squared (adj-R
resultsWe included 510,182 subjects alive on 31st December, 2015. Mean annual costs were 1894€ (standard deviation 9326€) (median 393€, IQ range 95€; 1480€), including zero-claim subjects. All models performed similarly after adjustment on demographics. RF model had better performances on other sets of covariates (pre-existing conditions, resource counts and past year costs). On full model, RF reached an adj-R
conclusionsRF should be preferred when the objective is to best predict medical costs. When the objective is to understand the contribution of predictors, GLM was well suited with demographics, conditions and base year cost.
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