用神经网络预测工伤赔偿案结案时长，提升管理效率。
Using Artificial Neural Networks to Predict Claim Duration in a Work Injury Compensation Environment
- 基于神经网络改进的Cox比例风险模型，融合伤情编码与人口信息。
- 输入索赔初始数据即可输出时长分布，支持缺失值处理。
- 适合工伤理赔系统用于早期风险评估与资源分配。
目前加拿大工伤赔偿机构使用国家工伤统计计划（NWISP）的标准编码系统记录伤情信息，这些编码详细记录了伤病的医疗属性和初始原因，可能蕴含预测伤情严重程度和工作缺勤时间的信息。索赔持续时间的测量与预测是工伤赔偿项目运作的核心。然而，由于编码体系复杂，传统统计建模方法价值有限。本文采用Ripley（1998年论文）提出的神经网络实现的Cox比例风险回归模型，构建工伤赔偿案件中索赔时长的预测模型。该模型以伤情编码、基本人口学及工作场所信息为输入，输出索赔时长的概率分布。输入信息可在索赔首次提交时获取，适用于索赔管理场景。文中还介绍了模型选择流程及对缺失协变量的处理方法。
原文摘要 · Abstract (English)
Currently, work injury compensation boards in Canada track injury information using a standard system of codes (under the National Work Injury Statistics Program (NWISP)). These codes capture the medical nature and original cause of the injury in some detail, hence they potentially contain information which may be used to predict the severity of an injury and the resulting time loss from work. Claim duration easurements and forecasts are central to the operation of a work injury compensation program. However, due to the complexity of the codes traditional statistical modelling techniques are of limited value. We will describe an artificial neural network implementation of Cox proportional hazards regression due to Ripley (1998 thesis) which is used as the basis for a model for the prediction of claim duration within a work injury compensation environment. The model accepts as input the injury codes, as well as basic demographic and workplace information. The output consists of a claim duration prediction in the form of a distribution. The input represents information available when a claim is first filed, and may therefore be used in a claims management setting. We will describe the model selection procedure, as well as a procedure for accepting inputs with missing covariates.
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