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Guiding Risk Adjustment Models Toward Machine Learning Methods
DOI:10.1001/jama.2023.12920.png)
摘要
En
期刊
J
IF:
55
论文数:
4.1W
被引数:
19.4W
机构
引用论文
A calibration hierarchy for risk models was defined: from utopia to empirical data定义了风险模型的校准层次结构: 从乌托邦到经验数据
Comparative Effectiveness of New Approaches to Improve Mortality Risk Models From Medicare Claims Data从医疗保险索赔数据改善死亡率风险模型的新方法的比较效果
JAMA NETWORK OPEN
IF9.7

