使用机器学习检测医疗保健索赔中的欺诈行为:系统审查
Anli du Preez1, Sanmitra Bhattacharya2, Peter Beling1
1Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States of America.
Artificial intelligence in medicine
|January 5, 2025
概括
机器学习有效地检测到医疗保险欺诈,而监督方法占主导地位. 数据不一致性和隐私等挑战需要进一步研究,包括先进的深度学习技术.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
- 欺诈检测 欺诈检测 欺诈检测
背景情况:
- 医疗欺诈造成的财务损失很大,估计占总支出的3%-10%.
- 机器学习 (ML) 为识别医疗保险索赔中的欺诈活动提供了强大的工具.
- 需要进行系统审查来分析过去二十年来在这个领域应用的ML技术.
研究的目的:
- 对用于检测医疗保险欺诈的机器学习技术进行系统的文献审查.
- 分析过去20年的方法,数据源和研究趋势.
- 识别现有挑战和未来在该领域的机遇.
主要方法:
- 搜索了主要的学术数据库 (例如谷歌学者,PubMed,IEEE Xplore) 进行相关研究.
- 包括介绍基于ML的方法对医疗保健索赔数据的实验结果的文章.
- 选择了137篇文章进行定性和定量分析.
主要成果:
- 基于ML的欺诈检测研究的激增,重点是提供商欺诈.
- 监督学习方法 (94项研究) 是最常见的,其次是无监督 (41) 和混合 (12) 方法.
- 传统的ML占主导地位,但深度学习的采用正在增加;大多数研究使用公共数据集,主要来自美国.
结论:
- 主要挑战包括数据不一致,缺乏标准化,隐私问题和有限的标签数据.
- 未来的研究应该优先考虑数据透明度,共享调查结果和基准数据集.
- 数据采样,特征编码和深度学习方面的进步对于提高欺诈检测效率至关重要.
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