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Articles 1 - 4 of 4
Full-Text Articles in Medical Specialties
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 …
Overcoming Small Data Limitations In Heart Disease Prediction By Using Surrogate Data, Alfeo Sabay, Laurie Harris, Vivek Bejugama, Karen Jaceldo-Siegl
Overcoming Small Data Limitations In Heart Disease Prediction By Using Surrogate Data, Alfeo Sabay, Laurie Harris, Vivek Bejugama, Karen Jaceldo-Siegl
SMU Data Science Review
In this paper, we present a heart disease prediction use case showing how synthetic data can be used to address privacy concerns and overcome constraints inherent in small medical research data sets. While advanced machine learning algorithms, such as neural networks models, can be implemented to improve prediction accuracy, these require very large data sets which are often not available in medical or clinical research. We examine the use of surrogate data sets comprised of synthetic observations for modeling heart disease prediction. We generate surrogate data, based on the characteristics of original observations, and compare prediction accuracy results achieved from …