论文标题
无监督的机器学习，用于可解释的医疗保健欺诈检测
Unsupervised Machine Learning for Explainable Health Care Fraud Detection
论文作者
论文摘要
美国联邦政府每年在医疗保健上花费超过一万亿美元，这在很大程度上由私人第三方提供，并由政府偿还。该系统中的一个主要问题是提供者的过度销售，浪费和欺诈，他们面临失去索赔的激励措施，以获得更高的付款。在本文中，我们开发了新颖的机器学习工具，以识别提供者过多的医疗保险，美国联邦成年人和残疾人的联邦健康保险计划。使用大规模的Medicare索赔数据，我们确定了与住院住院治疗中欺诈或过度灌输相一致的模式。我们提出的Medicare欺诈检测方法是完全无监督的，不依赖任何标记的培训数据，并且可以为最终用户解释，从而对标记提供者的潜在可疑行为提供了可疑行为。司法部的数据对面临反欺诈诉讼和几项案例研究的提供者的数据验证了我们的方法，并在定量和定性上都验证了我们的方法。
The US federal government spends more than a trillion dollars per year on health care, largely provided by private third parties and reimbursed by the government. A major concern in this system is overbilling, waste and fraud by providers, who face incentives to misreport on their claims in order to receive higher payments. In this paper, we develop novel machine learning tools to identify providers that overbill Medicare, the US federal health insurance program for elderly adults and the disabled. Using large-scale Medicare claims data, we identify patterns consistent with fraud or overbilling among inpatient hospitalizations. Our proposed approach for Medicare fraud detection is fully unsupervised, not relying on any labeled training data, and is explainable to end users, providing reasoning and interpretable insights into the potentially suspicious behavior of the flagged providers. Data from the Department of Justice on providers facing anti-fraud lawsuits and several case studies validate our approach and findings both quantitatively and qualitatively.
