Studies found that a small portion of the population spent the majority of health care resources, and they highlighted the importance of predicting high-cost users in the health care management and policy. Most prior research on high-cost user prediction models are based on diagnosis data with additional cost and health care utilization data to improve prediction accuracy. To further improve the prediction of high-cost users, researchers have been testing various new data sources such as self-reported health status data. In this study, we use three categories of medical check-up data, laboratory tests, self-reported medical history, and self-reported health behavior data to build high-cost user prediction models, and to assess the medical check-up features as predictors of high-cost users. Using three data-mining models, logistic regression, random forest, and neural network models, we show that under the diagnosis-based approach, medical check-up data marginally improve diagnosis-based prediction models. Under the cost-based approach, we find that medical check-up data improve cost-based prediction models marginally and medical check-up data can be a viable alternate data source to diagnosis data in predicting high-cost users.

中文翻译：

---

利用医疗检查数据改善对高成本医疗保健用户的预测。

研究发现，一小部分人花费了大部分医疗保健资源，他们强调了预测医疗保健管理和政策中高成本用户的重要性。以前有关高成本用户预测模型的大多数研究都是基于具有附加成本的诊断数据和医疗保健利用率数据来提高预测准确性。为了进一步改善对高成本用户的预测，研究人员一直在测试各种新数据源，例如自我报告的健康状况数据。在这项研究中，我们使用三类医疗检查数据，实验室检查，自我报告的病史和自我报告的健康行为数据来构建高成本的用户预测模型，并评估医疗检查功能，例如高成本用户的预测指标。使用三种数据挖掘模型，逻辑回归，随机森林和神经网络模型表明，在基于诊断的方法下，体检数据可略微改善基于诊断的预测模型。在基于成本的方法下，我们发现体检数据可以稍微改善基于成本的预测模型，并且体检数据可以作为预测高成本用户的诊断数据的可行替代数据源。