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Articles 1 - 7 of 7
Full-Text Articles in Medicine and Health Sciences
Exploration Of Data Science Toolbox And Predictive Models To Detect And Prevent Medicare Fraud, Waste, And Abuse, Benjamin P. Goodwin, Adam Canton, Babatunde Olanipekun
Exploration Of Data Science Toolbox And Predictive Models To Detect And Prevent Medicare Fraud, Waste, And Abuse, Benjamin P. Goodwin, Adam Canton, Babatunde Olanipekun
SMU Data Science Review
The Federal Department of Health and Human Services spends approximately $830 Billion annually on Medicare of which an estimated $30 to $110 billion is some form of fraud, waste, or abuse (FWA). Despite the Federal Government’s ongoing auditing efforts, fraud, waste, and abuse is rampant and requires modern machine learning approaches to generalize and detect such patterns. New and novel machine learning algorithms offer hope to help detect fraud, waste, and abuse. The existence of publicly accessible datasets complied by The Centers for Medicare & Medicaid Services (CMS) contain vast quantities of structured data. This data, coupled with industry standardized …
Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti
Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti
SMU Data Science Review
Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new …
Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel
Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel
SMU Data Science Review
Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model …
Machine Learning Approach To Distinguish Ulcerative Colitis And Crohn’S Disease Using Smote (Synthetic Minority Oversampling Technique) Methods, Kris Ghimire, Walter Lai, Yasser Omar, Thad Schwebke, Jamie Vo
Machine Learning Approach To Distinguish Ulcerative Colitis And Crohn’S Disease Using Smote (Synthetic Minority Oversampling Technique) Methods, Kris Ghimire, Walter Lai, Yasser Omar, Thad Schwebke, Jamie Vo
SMU Data Science Review
Irritable Bowel Disease (IBD) affects a sizable portion of the US population, causing symptoms such as vomiting, abdominal pain, and diarrhea. Despite the disease’s prevalence, the precise cause is not fully understood. This study consists of endoscopic and histological data from patients diagnosed with IBD and a control population for reference. The machine learning models' focus is to classify patients into IBD types. Several models were analyzed, including decision trees, logistic regression, and k-nearest neighbors. In addition, various methods of SMOTE were applied to determine the most effective transformation and ensuring that the dataset is balanced. The best model with …
Rule Out Screening For Undiagnosed Dementia And Alzheimer’S Disease Using An Ehr Based Machine Learning Solution, Branum Stephan, David A. Julovich, Dustin Bracy, Jeff Nguyen
Rule Out Screening For Undiagnosed Dementia And Alzheimer’S Disease Using An Ehr Based Machine Learning Solution, Branum Stephan, David A. Julovich, Dustin Bracy, Jeff Nguyen
SMU Data Science Review
Abstract. Current detection methods for Dementia and Alzheimer’s disease include cerebral spinal fluid (CSF) markers and/or the use of positron emission tomography (PET) imaging, both being high-cost, highly invasive testing methods. The need for low-cost, minimally invasive methods to prescreen individuals for cognitive impairment has been a challenge for many years. Today’s costs associated with an annual screen for all adults 65 and above using current methods (CSF, PET) reach well beyond trillions of dollars per year. Motivated by the limited accessibly and high costs, an alternative tool presented within this paper demonstrates an effective rule out screening for Dementia …
A Novel Methodology To Identify The Primary Topics Contained Within The Covid-19 Research Corpus, Allen Crane, Brock Freidrich, William Fehlman, Igor Frolow, Daniel W. Engels
A Novel Methodology To Identify The Primary Topics Contained Within The Covid-19 Research Corpus, Allen Crane, Brock Freidrich, William Fehlman, Igor Frolow, Daniel W. Engels
SMU Data Science Review
In this paper, we present a novel framework and system for the identification of primary research topics from within a corpus of related publications, the classification of individual publications according to these topics, and the results of the application of our framework and system to the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a corpus of published peer reviewed and pre-peer reviewed articles related to the coronavirus that causes COVID-19. Using machine learning techniques, such as Non-negative Matrix Factorization for Natural Language Processing and a Bayesian classifier, we developed a novel framework and system that automatically extracts sparse and meaningful …
Predicting Premature Birth Risk With Cfrna, Jason Lin, Jonathan Marin, John Santerre
Predicting Premature Birth Risk With Cfrna, Jason Lin, Jonathan Marin, John Santerre
SMU Data Science Review
Identifying which genes are early indicators for preterm births using cell-free ribonucleic acid (cfRNA) from non-invasive blood tests provided by pregnant women can improve prenatal care. Currently, there are no medical tests for early detection of preterm birth risk in routine checkups for pregnant women. Recent studies have shown potential genes that can predict preterm birth. Machine learning techniques are utilized to see if the Area Under the Curve (AUC) can be improved upon when evaluating the prediction accuracy for chosen genes sequences and concentrations. Using cell-free RNA data from non-invasive blood tests in conjunction with machine learning, we improve …