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利用医疗事故数据预测索赔频率：含随机效应的泊松过程模型AI 解读

I use the Florida state data base (1975–1987) of settled malpractice claims to develop Poisson process models for the frequency of claims filed against individual physicians. These models incorporate random effects and covariates that represent physician attributes and are natural generalizations of the negative binomial model that is typically used to study claims frequency. I predict claims frequencies during 1981–1982 using models that are selected and estimated from…

用医疗事故数据预测索赔频率：含随机效应的泊松过程模型AI 解读

I use the Florida state data base (1975–1987) of settled malpractice claims to develop Poisson process models for the frequency of claims filed against individual physicians. These models incorporate random effects and covariates that represent physician attributes and are natural generalizations of the negative binomial model that is typically used to study claims frequency. I predict claims frequencies during 1981–1982 using models that are selected and estimated from…