识别私人医疗保险中的高风险受益人
Adauto Santos1, Gislaine Camila Lapasini Leal1, Renato Balancieri2
1Technology Center, State University of Maringá, Maringá, Brazil.
Health informatics journal
|February 1, 2024
概括
这项研究使用机器学习来预测保险受益人中高成本医疗保健病例. K-近邻模型准确地确定了可能进入高风险群体的个体,提前几个月.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 预测高医疗费用和复杂病例对于私人医疗保险管理至关重要.
- 积极识别面临风险的受益人使得有针对性的干预和资源分配成为可能.
研究的目的:
- 在数据库中应用知识发现 (KDD) 过程,以确定可能属于"非常高成本"或"复杂病例"组的私人医疗保险受益人.
- 为了确定各种机器学习算法的预测准确度,用于此分类.
- 评估早期识别进入高风险群体的受益人的时间框架.
主要方法:
- 利用了数据库 (KDD) 过程中的知识发现.
- 开发了包含受益者健康计划利用率和人口特征的数据集.
- 评估了五种机器学习算法:随机森林,额外树,XGBoost,天真Bayes和K-最近邻居 (KNN).
主要成果:
- 该K-最近邻居 (KNN) 算法表现出强的性能,回忆率为81.12%,精度为83.77%,曲线下的面积 (AUC) 为0.9045.
- 受益人被归类为高风险群体的平均时间是他们第一次进入这种群体之前的8.11个月.
- 该研究分析了2019年1月至2020年6月的数据.
结论:
- 机器学习,特别是KNN,可以有效地预测私人保险受益人中高成本和复杂的医疗保健病例.
- 早期识别有风险的个体是可行的,允许主动的医疗保健管理策略.
- KDD过程为分析医疗保健利用数据提供了一个强大的框架,以改善风险分层.
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