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    New AI-powered computer model predicts disease progression during aging

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    Using artificial intelligence, a team of researchers at the University at Buffalo have developed a new system to model the progression of chronic disease with age in patients.

    Published in October Journal of Pharmacokinetics and Pharmacodynamics, The model evaluates metabolic and cardiovascular biomarkers. Cholesterol level, Body mass index, Glucose and Blood Pressure-Calculate the patient’s lifelong health and risk of illness.

    The results of this study are very important because of the increased risk of developing metabolic and cardiovascular disease with aging. This process adversely affects cellular, psychological, and behavioral processes.

    “There is an unmet need for a scalable approach that can provide lifelong pharmaceutical care guidance in the presence of aging and chronic comorbidity,” said lead author, UB School’s professor of pharmaceutical science. Dr. Murali Ramanathan said. Of pharmaceutical science. “This knowledge gap could potentially be filled by innovative disease progression modeling.”

    This model facilitates the evaluation of long-term chronic medications and helps clinicians monitor treatment responses for conditions that become more frequent with age, such as diabetes, high cholesterol, and high blood pressure, says Ramanasan.

    Additional researchers include Mason McComb, Ph.D., lead author and graduate of the UB School of Pharmacy and Pharmaceutical Sciences. It is included. Dr. Rachael Hageman Blair, Associate Professor of Biostatistics at the UB School of Public Health and Health Professionals. Dr. Martin Lissie is an associate professor of statistics and actuarial science at the University of Waterloo.

    In the survey, we surveyed three data Case Study Within the 3rd National Health and Nutrition Examination Survey (NHANES), which evaluated metabolic and cardiovascular biomarkers in approximately 40,000 people in the United States.

    Biomarkers, including measurements such as temperature, body weight And height are used to diagnose, treat and monitor overall health and many illnesses.

    Researchers examined seven metabolic biomarkers: body mass index, waist-hip ratio, total cholesterol, high-density lipoprotein cholesterol, triglyceride, glucose, and glycohemoglobin.Cardiovascular biomarkers tested include systole and diastole blood pressure, Pulse rate and homocysteine.

    The model “learns” how aging affects these measurements by analyzing changes in metabolism and cardiovascular biomarkers.When Machine learning, The system uses previous biomarker-level memory to predict future measurements. This ultimately reveals how metabolic and cardiovascular disease progresses over time.

    Higher physical activity is associated with better metabolic health risk factor profiles in menopausal women

    For more information:
    Mason McComb et al, Machine Learning-Based Biomarker-Based System Pharmacology for Big Data: Modeling Probability Theory of Natural History and Disease Progression, Journal of Pharmacokinetics and Pharmacodynamics (2021). DOI: 10.1007 / s10928-021-09786-5

    Provided by
    Buffalo University

    Quote: Computer model using new AI is aging acquired on December 7, 2021 from https: // (December 2021) Predict the progression of the disease during (7th)

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    New AI-powered computer model predicts disease progression during aging Source link New AI-powered computer model predicts disease progression during aging

    The post New AI-powered computer model predicts disease progression during aging appeared first on California News Times.

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