Luo Li, Li Jialing, Lian Shuhao, Zeng Xiaoxi, Sun Lin, Li Chunyang, Huang Debin, Zhang Wei
Sichuan University, China.
West China Hospital of Sichuan University, China.
Health Informatics J. 2020 Sep;26(3):1577-1598. doi: 10.1177/1460458219881335. Epub 2019 Nov 11.
The accurate identification and prediction of high-cost Chronic obstructive pulmonary disease (COPD) patients is important for addressing the economic burden of COPD. The objectives of this study were to use machine learning approaches to identify and predict potential high-cost patients and explore the key variables of the forecasting model, by comparing differences in the predictive performance of different variable sets. Machine learning approaches were used to estimate the medical costs of COPD patients using the Medical Insurance Data of a large city in western China. The prediction models used were logistic regression, random forest (RF), and extreme gradient boosting (XGBoost). All three models had good predictive performance. The XGBoost model outperformed the others. The areas under the ROC curve for Logistic Regression, RF and XGBoost were 0.787, 0.792 and 0.801. The precision and accuracy metrics indicated that the methods achieved correct and reliable results. The results of this study can be used by healthcare data analysts, policy makers, insurers, and healthcare planners to improve the delivery of health services.
准确识别和预测高成本慢性阻塞性肺疾病（COPD）患者对于应对COPD的经济负担至关重要。本研究的目的是通过比较不同变量集预测性能的差异，使用机器学习方法识别和预测潜在的高成本患者，并探索预测模型的关键变量。利用中国西部某大城市的医疗保险数据，采用机器学习方法估算COPD患者的医疗费用。使用的预测模型有逻辑回归、随机森林（RF）和极端梯度提升（XGBoost）。所有三个模型都具有良好的预测性能。XGBoost模型表现优于其他模型。逻辑回归、RF和XGBoost的ROC曲线下面积分别为0.787、0.792和0.801。精度和准确性指标表明这些方法取得了正确可靠的结果。本研究结果可供医疗保健数据分析师、政策制定者、保险公司和医疗保健规划者用于改善医疗服务的提供。
