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Medical Provider Embeddings for Healthcare Fraud Detection

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Abstract

Advances in data mining and machine learning continue to transform the healthcare industry and provide value to medical professionals and patients. In this study, we address the problem of encoding medical provider types and present four techniques for learning dense, semantic embeddings that capture provider specialty similarities. The first two methods (GloVe and Med-W2V) use pre-trained word embeddings to convert provider specialty descriptions to phrase embeddings. Next, HcpsVec and RxVec embeddings are constructed from publicly available big data using specialty-procedure and specialty-drug occurrence matrices, respectively. We evaluate the learned provider type embeddings on two real-world medicare fraud classification problems using logistic regression (LR), random forest (RF), gradient boosted tree (GBT), and multilayer perceptron (MLP) learners. Through repetition, statistical analysis, and feature importance measures, we confirm that semantic embeddings for provider types significantly improve fraud classification results. Finally, t-SNE visualizations are used to show that the learned provider type embeddings capture meaningful specialty characteristics and provider type similarities. Our primary contributions are two novel methods for encoding medical specialties using procedure-level statistics and the evaluation of four encoding techniques on two large-scale healthcare fraud classification tasks. Since all data sources are publicly available, these encoding techniques can be readily adopted and applied in future machine learning applications in the healthcare industry.

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Data Availability

All data analysed during this study are referenced in this published article.

Code Availability

All software packages used during this study are open-source and are referenced in this published article.

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Acknowledgements

The authors would like to thank the reviewers in the Data Mining and Machine Learning Laboratory at Florida Atlantic University.

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  1. College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA

    Justin M. Johnson & Taghi M. Khoshgoftaar

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  1. Justin M. Johnson
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Contributions

JMJ performed the literature review, executed the experiment design, and drafted the manuscript. TMK worked with JMJ to develop the article’s framework and focus. All authors have read and approved the final manuscript.

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Correspondence to Justin M. Johnson.

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This article is part of the topical collection “Artificial Intelligence for HealthCare” guest-edited by Lydia Bouzar-Benlabiod, Stuart H. Rubin and Edwige Pissaloux.

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Johnson, J.M., Khoshgoftaar, T.M. Medical Provider Embeddings for Healthcare Fraud Detection. SN COMPUT. SCI. 2, 276 (2021). https://doi.org/10.1007/s42979-021-00656-y

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