使用无监督深度学习方法从医疗保健索赔中检测程序代码的过度利用
Michael Suesserman1, Samantha Gorny2, Daniel Lasaga2
1AI Center of Excellence, Deloitte & Touche LLP, New York, NY, USA.
BMC medical informatics and decision making
|September 28, 2023
概括
这项研究使用无监督的深度学习来检测医疗索赔中的欺诈,浪费和滥用 (FWA),通过识别过度使用的程序代码. 自动编码模型有效地识别了不合理的程序,提高了医疗保健索赔的准确性.
科学领域:
- 机器学习 机器学习
- 医疗保健分析 医疗保健分析
- 数据科学数据科学数据科学
背景情况:
- 医疗索赔中的欺诈,浪费和滥用 (FWA) 对医疗保健质量和成本产生负面影响.
- 程序代码过度使用,即程序与诊断或患者个人资料无关,是FWA的主要组成部分.
- 识别不合理的程序对于减轻医疗保健索赔中的FWA至关重要.
研究的目的:
- 通过无监督机器学习,在数以百万计的医疗保险索赔中识别不合理的程序代码.
- 应用深度自动编码器并将其有效性与基于密度的集群模型进行比较,用于FWA检测.
- 为应对在没有标记数据的情况下识别FWA的挑战.
主要方法:
- 使用深度自动编码器来检测在医疗保健索赔中标志着FWA的异常程序代码.
- 雇员诊断,程序和人口统计数据作为机器学习模型的特征.
- 将自动编码器性能与使用100,000和3300万索赔数据集的基线密度集群模型进行比较.
主要成果:
- 自动编码器模型,特别是具有新的特征加权损失函数的自动编码器模型,超过了基于密度的集群方法.
- 使用合成和手动注释的异常值数据集来评估性能.
- 在3300万索赔数据集中,自动编码器在手动注释的数据上分别获得了0.48,0.90和0.63的精度,回忆和F1分数.
结论:
- 无监督的深度学习方法可用于识别医疗保健索赔中的潜在程序过度利用.
- 开发的自动编码器模型在检测FWA指标方面表现出有效性.
- 这种方法为提高医疗计费的完整性和降低医疗保健成本提供了一个有希望的解决方案.
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