AIMC Topic: United States

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Artificial intelligence in sleep medicine: an American Academy of Sleep Medicine position statement.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Apr 15, 2020
Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial application in the sleep disorders center is the assisted (or enhanced) scoring of sleep and associ...
Sleep Wake Disorders Academies and Institutes United States Sleep Polysomnography Humans Artificial Intelligence
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On Patient Safety: The Lure of Artificial Intelligence-Are We Jeopardizing Our Patients' Privacy?

Clinical orthopaedics and related research Apr 1, 2020
Privacy Forms and Records Control United States Health Insurance Portability and Accountability Act Health Records, Personal Humans Artificial Intelligence Patient Safety
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ESMRMB Round Table report on "Can Europe Lead in Machine Learning of MRI-Data?".

Magma (New York, N.Y.) Apr 1, 2020
Privacy European Union Image Processing, Computer-Assisted United States Magnetic Resonance Imaging Machine Learning Humans Diagnosis, Computer-Assisted Prognosis China
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Regulatory oversight, causal inference, and safe and effective health care machine learning.

Biostatistics (Oxford, England) Apr 1, 2020
In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S. Food and Drug Administration (FDA) has b...
Humans Causality United States Food and Drug Administration Health Services Research Medical Informatics Applications Machine Learning Delivery of Health Care United States
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Multiple Plasma Biomarkers for Risk Stratification in Patients With Heart Failure and Preserved Ejection Fraction.

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).
Heart Failure Middle Aged Risk Assessment United States Female Aged Male Humans Machine Learning Biomarkers
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Machine Learning Uncovers Food- and Excipient-Drug Interactions.

Cell reports Mar 17, 2020
Inactive ingredients and generally recognized as safe compounds are regarded by the US Food and Drug Administration (FDA) as benign for human consumption within specified dose ranges, but a growing body of research has revealed that many inactive ing...
Humans Machine Learning United States Food and Drug Administration ATP Binding Cassette Transporter, Subfamily B, Member 1 Excipients Hep G2 Cells Swine Food Drug Interactions Mice, Inbred BALB C Retinyl Esters Diterpenes United States Pharmaceutical Preparations Biological Assay Glucuronosyltransferase Female Abietanes Animals
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The AI Era: The Role of Medical Imaging and Radiation Therapy Professionals.

Radiologic technology Mar 1, 2020
Surveys and Questionnaires United States Diagnostic Imaging Quality Assurance, Health Care Humans Artificial Intelligence Radiotherapy
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Artificial Intelligence in Health Care: A Report From the National Academy of Medicine.

JAMA Feb 11, 2020
Humans Artificial Intelligence Delivery of Health Care National Academies of Science, Engineering, and Medicine, U.S., Health and Medicine Division Trust United States Bias
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Putting the world back to work: An expert system using big data and artificial intelligence in combating the spread of COVID-19 and similar contagious diseases.

Work (Reading, Mass.) Jan 1, 2020
BACKGROUND: To combat COVID-19, curb the pandemic, and manage containment, governments around the world are turning to data collection and population monitoring for analysis and prediction. The massive data generated through the use of big data and a...
United States Occupational Safety and Health Administration Return to Work Pneumonia, Viral Big Data Pandemics Occupational Health Humans United States COVID-19 Expert Systems Artificial Intelligence Coronavirus Infections Delivery of Health Care Work Machine Learning
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A Machine Learning Approach to Identification of Unhealthy Drinking.

Journal of the American Board of Family Medicine : JABFM Jan 1, 2020
INTRODUCTION: Unhealthy drinking is prevalent in the United States, and yet it is underidentified and undertreated. Identifying unhealthy drinkers can be time-consuming and uncomfortable for primary care providers. An automated rule for identificatio...
United States Mass Screening Support Vector Machine Male Logistic Models Nutrition Surveys Alcohol Drinking Humans Machine Learning Female Adult
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