Dementia risk predictions from German claims data using methods of machine learning.
Alzheimers Dement
; 19(2): 477-486, 2023 02.
Article
em En
| MEDLINE
| ID: mdl-35451562
INTRODUCTION: We examined whether German claims data are suitable for dementia risk prediction, how machine learning (ML) compares to classical regression, and what the important predictors for dementia risk are. METHODS: We analyzed data from the largest German health insurance company, including 117,895 dementia-free people age 65+. Follow-up was 10 years. Predictors were: 23 age-related diseases, 212 medical prescriptions, 87 surgery codes, as well as age and sex. Statistical methods included logistic regression (LR), gradient boosting (GBM), and random forests (RFs). RESULTS: Discriminatory power was moderate for LR (C-statistic = 0.714; 95% confidence interval [CI] = 0.708-0.720) and GBM (C-statistic = 0.707; 95% CI = 0.700-0.713) and lower for RF (C-statistic = 0.636; 95% CI = 0.628-0.643). GBM had the best model calibration. We identified antipsychotic medications and cerebrovascular disease but also a less-established specific antibacterial medical prescription as important predictors. DISCUSSION: Our models from German claims data have acceptable accuracy and may provide cost-effective decision support for early dementia screening.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado de Máquina
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Seguro Saúde
Tipo de estudo:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Humans
Idioma:
En
Revista:
Alzheimers Dement
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Alemanha