数据驱动的医疗保险系统使用机器学习和区块链技术
Irum Matloob1, Shoab Khan2, Bushra Bashir1
1Department of Software Engineering, Fatima Jinnah Women University, Rawalpindi, Pakistan.
PeerJ. Computer science
|September 24, 2025
概括
这项研究引入了一个用于医疗保健建议和保险欺诈检测的智能系统,将机器学习和区块链结合起来,以确保索赔安全和改善服务质量. 该系统成功地降低了医院费用,并提高了保险流程的透明度.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 区块链技术 区块链技术
背景情况:
- 医疗保险欺诈问题越来越严重,需要智能系统来监控服务并防止欺诈活动.
- 现有的系统缺乏全面的功能,无论是基于质量的医疗保健建议还是自动化,安全的保险索赔处理.
研究的目的:
- 根据服务质量开发一个智能系统来推医生和保险计划.
- 整合区块链技术,以实现医疗保险索赔退款的安全和透明自动化.
- 打击医疗保险欺诈,减少相关支出.
主要方法:
- 该系统结合了Singular Value Decomposition (SVD),基于K-Nearest Neighbors (KNN) 的协作过,基于项目的协作过,基于内容的过的术语频率-反向文档频率 (TF-IDF) 和K-means集群.
- 同位数相似性用于测量患者与医生的相似性,以获得精细的建议.
- 以太坊区块链上的智能合约技术用于自动化保险索赔赔偿.
主要成果:
- 推系统实现了0.478的根平均平方误差 (RMSE) 和0.0422.2的平均绝对误差 (MAE).
- 拟议的系统导致当地医院的总体支出减少.
- 区块链集成提高了安全性和透明度,大大降低了医疗欺诈事件.
结论:
- 开发的智能系统有效地推医疗保健提供者并优化保险计划,从而节省成本.
- 区块链技术为医疗保险索赔提供了一个安全,透明和高效的平台,减轻欺诈行为.
- 这种新的系统解决了巴基斯坦对综合医疗保健建议和欺诈预防解决方案的迫切需要.
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