INTRODUCTION: The purpose of the current study is to determine the accuracy of machine learning in predicting bleeding outcomes post percutaneous coronary intervention (PCI) in comparison with the American College of Cardiology CathPCI bleeding risk ...
OBJECTIVE: The present study aimed to identify and prioritize control measures of violence against health care workers (HWs) using the fuzzy analytical hierarchy process (FAHP) and fuzzy additive ratio assessment (ARAS-F).
Clinical and translational gastroenterology
Apr 1, 2020
OBJECTIVES: We lack reliable methods for identifying patients with chronic pancreatitis (CP) at increased risk for pancreatic cancer. We aimed to identify radiographic parameters associated with pancreatic cancer in this population.
OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning...
Journal of the American Medical Informatics Association : JAMIA
Apr 1, 2020
OBJECTIVE: Implementation of machine learning (ML) may be limited by patients' right to "meaningful information about the logic involved" when ML influences healthcare decisions. Given the complexity of healthcare decisions, it is likely that ML outp...
Journal of the American Medical Informatics Association : JAMIA
Apr 1, 2020
OBJECTIVE: Reliable longitudinal risk prediction for hospitalized patients is needed to provide quality care. Our goal is to develop a generalizable model capable of leveraging clinical notes to predict healthcare-associated diseases 24-96 hours in a...
Journal of the American College of Cardiology
Mar 24, 2020
BACKGROUND: Better risk stratification strategies are needed to enhance clinical care and trial design in heart failure with preserved ejection fraction (HFpEF).
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