Beaumont Health

About: Beaumont Health is a nonprofit organization based out in Royal Oak, Michigan, United States. It is known for research contribution in the topics: Medicine & Population. The organization has 1483 authors who have published 1448 publications receiving 15407 citations. The organization is also known as: William Beaumont Health System & Beaumont Hospitals.

Addressing challenges in underactive bladder: recommendations and insights from the Congress on Underactive Bladder (CURE-UAB)

Abstract: Underactive bladder (UAB) is an expanding troublesome health issue, exerting a major influence on the health and independence of older people with a disproportionally low level of attention received. The 2nd International Congress on Underactive Bladder (CURE-UAB 2) convened in Denver, CO on December 3 and 4, 2015, and comprised of top clinicians, scientists, and other stakeholders to address the challenges in UAB. A series of workshops aimed to define UAB and its phenotype, define detrusor underactivity (DU) and create a subtyping of DU, evaluate existing animal models for DU, and lastly to establish research priorities for UAB.

Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19.

Abstract: BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide. OBJECTIVES: To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. METHODS: Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients' data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort. RESULTS: Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%. CONCLUSION: Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.

Invasive Versus Echocardiographic Evaluation of Transvalvular Gradients Immediately Post-Transcatheter Aortic Valve Replacement.

Abstract: July 2019 1