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Showing 1 to 9 of 9 for “"Healthcare analytics"”.
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Applications of healthcare analytics in reducing hospitalization days
In this thesis, we employ healthcare analytics to inform system-level changes at Massachusetts General Hospital that could lead to a significant reduction in avoidable hospitalization days and improvement in patients outcomes. The first area of focus is around avoidable bed-days in the ICU. Many …
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Enhancing Risk Stratification for Substance Use Disorder, Depression, and Anxiety through Quantitative Predictive Analytics
… to mitigate these conditions' societal and healthcare burdens effectively. Employing a quantitative methodology, this study focuses on predicting individuals at risk for substance use disorder, depression, or anxiety using health plan data. The research utilizes several machine learning …
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Personalized Decision Modeling for Intervention and Prevention of Cancers
… (HPV), representatively. Three popular healthcare analytics techniques, Markov models, regression-based predictive models, and discrete-event simulation, are developed in the context of personalized cancer medicine. We discuss multiple possibilities of incorporating patient-specific risk …
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DATA GOVERNANCE FRAMEWORK FOR ML-BASED, DATA-INTENSIVE DISTRIBUTED SYSTEMS
… parties collaborate to deliver sophisticated analytics capabilities. Machine Learning (ML) and Internet of Things (IoT) technologies are increasingly integrated into these systems, enabling advanced data processing pipelines that span multiple organizational boundaries. Modern distributed data …
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Predicting the Effects of Sedative Infusion on Acute Traumatic Brain Injury Patients
Healthcare analytics has traditionally relied upon linear and logistic regression models to address clinical research questions mostly because they produce highly interpretable results [1, 2]. These results contain valuable statistics such as p-values, coefficients, and odds ratios that provide …
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Joint clustering of hospitals based on their adminission behavior for different diseases using network of networks data model
Healthcare analytics is a rapidly growing industry where health organizations integrate data-driven insights into their clinical and operational decisions. One such insight involves clustering hospitals based on similarities in their monthly admission behavior, which can help ensure that the supply …
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Outcome prediction and structure discovery in healthcare data
… learning, and the continually increasing cost of healthcare in the United States drive the necessity of algorithmic solutions with the potential to improve patient care and reduce healthcare costs. Such algorithms can enable the identification of the most relevant parameters for predicting adverse …
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Analytics under Variability, Volume, and Velocity with Applications to Sustainability and Healthcare
Analytics, machine learning, and optimization provide unique opportunities to harness the massive amounts of data that are available and positively impact some of the most pressing challenges of our time, including climate change and improved healthcare operations. The classical paradigm of …