AIMC Topic: Risk Assessment

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Machine Learning on High-Dimensional Data to Predict Bleeding Post Percutaneous Coronary Intervention.

The Journal of invasive cardiology
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 ...

Strategies for controlling violence against health care workers: Application of fuzzy analytical hierarchy process and fuzzy additive ratio assessment.

Journal of nursing management
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).

Identification of Individuals at Increased Risk for Pancreatic Cancer in a Community-Based Cohort of Patients With Suspected Chronic Pancreatitis.

Clinical and translational gastroenterology
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.

Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.

Critical care medicine
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...

Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator.

Journal of the American Medical Informatics Association : JAMIA
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...

A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision.

Journal of the American Medical Informatics Association : JAMIA
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...

Multiple Plasma Biomarkers for Risk Stratification in Patients With Heart Failure and Preserved Ejection Fraction.

Journal of the American College of Cardiology
BACKGROUND: Better risk stratification strategies are needed to enhance clinical care and trial design in heart failure with preserved ejection fraction (HFpEF).