Parallelized Latent Dirichlet Allocation for Medical Fraud Detection

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dc.contributor.advisorLaKomski, Gregory
dc.contributor.advisorEkin, Tahir
dc.contributor.authorReady, Joshua
dc.date.accessioned2022-01-24T15:38:14Z
dc.date.available2022-01-24T15:38:14Z
dc.date.issued2020-12
dc.description.abstractThis paper seeks to analyze topic modeling software that assists investigators search for potential fraudulent Medicare billing activities. Furthermore, it will document the research done to parallelize this software in order to reduce its computational processing time. This paper also seeks to study questions such as “do medical providers of a specific type have similar billing patterns across states and regions?” and “can semantic analysis be used to identify both common and outlier billing patterns for specific providers?” According to the Blue Cross, medical fraud costs the United States government $68 billion every year. This project focused on improving the speed and usability of the existing software to combat this problem.
dc.description.departmentComputer Science
dc.formatText
dc.format.extent14 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationReady, J. C. (2020). Parallelized latent Dirichlet allocation for medical fraud detection (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/15190
dc.language.isoen
dc.subjectLatent Dirichlet Allocation
dc.subjectLDA
dc.subjecttopic modeling
dc.subjecttopic clustering
dc.subjectCUDA
dc.subjectmedical fraud detection
dc.subjectmedicare fraud
dc.subjectparallel programming
dc.titleParallelized Latent Dirichlet Allocation for Medical Fraud Detection
dc.typeThesis
thesis.degree.departmentHonors College
thesis.degree.disciplineComputer Science
thesis.degree.grantorTexas State University

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