cirpublications.com-aRgv-1790878114-k755077157 1790878114 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2024 3 2 Wei Liu Zhang Min 2024 10.68159/k755077157 https://cirpublications.com/pub/journal/1/article/k755077157 Bauder RA, Khoshgoftaar TM, Seliya N. A survey on the state of healthcare upcoding fraud analysis and detection. Health Serv Outcomes Res Methodol. 2017;17(1):31-55. Bauder RA, Khoshgoftaar TM. Multivariate outlier detection in medicare claims payments applying probabilistic programming methods. Health Serv Outcomes Res Methodol. 2017;17(3):256-89. Bauder RA, Khoshgoftaar TM. Medicare fraud detection using machine learning methods. In: Proc IEEE 16th Int Conf Mach Learn Appl. 2017:858-65. Bauder RA, Khoshgoftaar TM. The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data. Health Inf Sci Syst. 2018;6(1):9. Bauder RA, Khoshgoftaar TM. A survey of medicare data processing and integration for fraud detection. In: Proc IEEE Int Conf Inf Reuse Integr. 2018:9-14. Bauder RA, da Rosa R, Khoshgoftaar TM. Identifying medicare provider fraud with unsupervised machine learning. In: Proc IEEE Int Conf Inf Reuse Integr Data Sci. 2018:285-92. Bauder RA, Khoshgoftaar TM. Medicare fraud detection using random forest with class imbalanced big data. In: Proc IEEE Int Conf Inf Reuse Integr Data Sci. 2018:80-7. Bauder RA, Khoshgoftaar TM, Hasanin T. Data sampling approaches with severely imbalanced big data for medicare fraud detection. In: Proc IEEE Int Conf Tools Artif Intell. 2018:137-42. Herland M, Bauder RA, Khoshgoftaar TM. The effects of class rarity on the evaluation of supervised healthcare fraud detection models. J Big Data. 2019;6(1):21. Johnson JM, Khoshgoftaar TM. Medicare fraud detection using neural networks. J Big Data. 2019;6(1):63. Johnson JM, Khoshgoftaar TM. Deep learning and data sampling with imbalanced big data. In: Proc IEEE 20th Int Conf Inf Reuse Integr Data Sci. 2019:175-83. Johnson JM, Khoshgoftaar TM. Deep learning and thresholding with class-imbalanced big data. In: Proc IEEE Int Conf Mach Learn Appl. 2019:755-62. Johnson JM, Khoshgoftaar TM. Deep learning and data sampling with imbalanced big data. In: Proc IEEE 20th Int Conf Inf Reuse Integr Data Sci. 2019:175-83. Herland M, Bauder RA, Khoshgoftaar TM. Medical provider specialty predictions for the detection of anomalous medicare insurance claims. In: Proc IEEE Int Conf Inf Reuse Integr. 2017:579-88. Johnson JM, Khoshgoftaar TM. Data-centric AI for healthcare fraud detection. SN Comput Sci. 2023;4(4):389. Settipalli L, Gangadharan GR. WMTDBC: An unsupervised multivariate analysis model for fraud detection in health insurance claims. Expert Syst Appl. 2023;215:119259. Yoo Y, Shin J, Kyeong S. Medicare fraud detection using graph analysis: a comparative study of machine learning and graph neural networks. IEEE Access. 2023;11:77493-503. Hancock JT, Bauder RA, Wang H, Khoshgoftaar TM. Explainable machine learning models for medicare fraud detection. J Big Data. 2023;10(1):154. Johnson JM, Khoshgoftaar TM. Healthcare provider summary data for fraud classification. In: Proc IEEE 23rd Int Conf Inf Reuse Integr Data Sci. 2022;236-42. Hancock JT, Khoshgoftaar TM. Optimizing ensemble trees for big data healthcare fraud detection. In: Proc IEEE 23rd Int Conf Inf Reuse Integr Data Sci. 2022:243-9. Hancock JT, Khoshgoftaar TM. The effects of random undersampling for big data medicare fraud detection. In: Proc IEEE Int Conf Serv-Oriented Syst Eng. 2022:141-6. Hancock JT, Khoshgoftaar TM. Hyperparameter tuning for medicare fraud detection in big data. SN Comput Sci. 2022;3(6):440. Yoo Y, Shin D, Han D, Kyeong S, Shin J. Medicare fraud detection using graph neural networks. In: Proc Int Conf Electr Comput Energy Technol. 2022;1-5. Kumaraswamy N, Markey MK, Ekin T, Barner JC, Rascati K. Healthcare fraud data mining methods: A look back and look ahead. Perspect Health Inf Manag. 2022;19(1):1i. Johnson JM, Khoshgoftaar TM. Encoding high-dimensional procedure codes for healthcare fraud detection. SN Comput Sci. 2022;3(5):362. Kumaraswamy N, Markey MK, Barner JC, Rascati K. Feature engineering to detect fraud using healthcare claims data. Expert Syst Appl. 2022;210:118433. Nugraha RA, Pardede HF, Subekti A. Oversampling based on generative adversarial networks to overcome imbalance data in predicting fraud insurance claim. Kuwait J Sci. 2023;50(1B):1-7. Li J, Liu J, Liu X, Yang F, Xu Y. A medical insurance fraud detection model with knowledge graph and machine learning. In: Proc Int Conf Comput Appl Inf Secur. 2022:531-40. Settipalli L, Gangadharan GR. Provider profiling and labeling of fraudulent health insurance claims using weighted MultiTree. J Ambient Intell Humaniz Comput. 2023;14:11471-93. Leevy JL, Hancock J, Khoshgoftaar TM, Abdollah Zadeh A. Investigating the effectiveness of one-class and binary classification for fraud detection. J Big Data. 2023;10(1):157. Matloob I, Khan SA, Rahman HU. Sequence mining and prediction-based healthcare fraud detection methodology. IEEE Access. 2020;8:143256-73. 10.1109/ACCESS.2020.3013962 Sowah RA, Kuuboore M, Ofoli A, Kwofie S, Asiedu L, Koumadi KM, et al. Decision support system for fraud detection in health insurance claims using genetic support vector machines. J Eng. 2019;2019:1432597.