AIMC Topic: United States
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Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study.
The journal of trauma and acute care surgery
Jan 1, 2022
INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive model...
Risk of Developing Breast Reconstruction Complications: A Machine-Learning Nomogram for Individualized Risk Estimation with and without Postmastectomy Radiation Therapy.
Plastic and reconstructive surgery
Jan 1, 2022
BACKGROUND: The purpose of this study was to create a nomogram using machine learning models predicting risk of breast reconstruction complications with or without postmastectomy radiation therapy.
When Medical Devices Have a Mind of Their Own: The Challenges of Regulating Artificial Intelligence.
American journal of law & medicine
Dec 1, 2021
How can an agency like the U.S. Food & Drug Administration ("FDA") effectively regulate software that is constantly learning and adapting to real-world data? Continuously learning algorithms pose significant public health risks if a medical device ca...
A Machine Learning Approach to Reclassifying Miscellaneous Patient Safety Event Reports.
Journal of patient safety
Dec 1, 2021
BACKGROUND AND OBJECTIVES: Medical errors are a leading cause of death in the United States. Despite widespread adoption of patient safety reporting systems to address medical errors, making sense of the reports collected in these systems is challeng...
Kidney Allocation Policy: Past, Present, and Future.
Advances in chronic kidney disease
Nov 1, 2021
Despite an increase in the number of kidney transplants performed annually, there remain more than 90,000 individuals awaiting transplantation in the United States. As kidney transplantation has evolved, so has kidney allocation policies. The Kidney ...
Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence.
Open heart
Nov 1, 2021
OBJECTIVE: The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT).
Artificial intelligence: Optimizing patient care in acute and postacute settings.
Nursing management
Nov 1, 2021
Regulatory Issues and Challenges to Artificial Intelligence Adoption.
Radiologic clinics of North America
Nov 1, 2021
Artificial intelligence technology promises to redefine the practice of radiology. However, it exists in a nascent phase and remains largely untested in the clinical space. This nature is both a cause and consequence of the uncertain legal-regulatory...
Development and Validation of a Machine Learning-Based Decision Support Tool for Residency Applicant Screening and Review.
Academic medicine : journal of the Association of American Medical Colleges
Nov 1, 2021
PURPOSE: Residency programs face overwhelming numbers of residency applications, limiting holistic review. Artificial intelligence techniques have been proposed to address this challenge but have not been created. Here, a multidisciplinary team sough...
Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients.
Open heart
Oct 1, 2021
OBJECTIVES: Identifying high-risk patients is crucial for effective cardiovascular disease (CVD) prevention. It is not known whether electronic health record (EHR)-based machine-learning (ML) models can improve CVD risk stratification compared with a...
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