AIMC Topic: Disease Management

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Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease.

European radiology May 28, 2020
OBJECTIVES: This study investigated the impact of machine learning (ML)-based fractional flow reserve derived from computed tomography (FFR) compared to invasive coronary angiography (ICA) for therapeutic decision-making and patient outcome in patien...
Decision Making Predictive Value of Tests Coronary Angiography Male Middle Aged Severity of Illness Index Fractional Flow Reserve, Myocardial Coronary Artery Disease Disease Management Computed Tomography Angiography Humans Artificial Intelligence Machine Learning Retrospective Studies Female
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Machine Learning (ML) based-method applied in recurrent pregnancy loss (RPL) patients diagnostic work-up: a potential innovation in common clinical practice.

Scientific reports May 14, 2020
RPL is a very debated condition, in which many issues concerning definition, etiological factors to investigate or therapies to apply are still controversial. ML could help clinicians to reach an objectiveness in RPL classification and access to care...
Humans Machine Learning Algorithms Middle Aged Female Disease Management Adolescent Young Adult Abortion, Habitual Support Vector Machine Biomarkers Adult Clinical Decision-Making Pregnancy
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A Machine Learning Approach to Management of Heart Failure Populations.

JACC. Heart failure May 6, 2020
BACKGROUND: Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies.
Aged, 80 and over Male Middle Aged Retrospective Studies Female Aged Population Surveillance Humans Morbidity Machine Learning Risk Assessment Heart Failure ROC Curve Disease Management United States
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Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials.

European radiology Mar 12, 2020
OBJECTIVE: To determine the potential impact of on-site CT-derived fractional flow reserve (CT-FFR) on the diagnostic efficiency and effectiveness of coronary CT angiography (CCTA) in patients with obstructive coronary artery disease (CAD) on CCTA.
Fractional Flow Reserve, Myocardial Cardiac Catheterization Computed Tomography Angiography Coronary Stenosis Percutaneous Coronary Intervention Coronary Artery Disease Disease Management Female Aged Male Cohort Studies Coronary Angiography Middle Aged Hemodynamics Machine Learning Retrospective Studies Tomography, X-Ray Computed Humans
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Clinical management of sepsis can be improved by artificial intelligence: no.

Intensive care medicine Feb 3, 2020
Sepsis Decision Support Systems, Clinical Humans Artificial Intelligence Disease Management Quality of Health Care
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Clinical management of sepsis can be improved by artificial intelligence: yes.

Intensive care medicine Dec 13, 2019
Sepsis Decision Support Systems, Clinical Disease Management Humans Artificial Intelligence
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Standard operating procedure for curation and clinical interpretation of variants in cancer.

Genome medicine Nov 29, 2019
Manually curated variant knowledgebases and their associated knowledge models are serving an increasingly important role in distributing and interpreting variants in cancer. These knowledgebases vary in their level of public accessibility, and the co...
Humans Neoplasms Clinical Competence Practice Patterns, Physicians' Disease Susceptibility Models, Theoretical Knowledge Bases Disease Management
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Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success.

Current neurology and neuroscience reports Nov 13, 2019
PURPOSE OF REVIEW: Neurocritical care combines the complexity of both medical and surgical disease states with the inherent limitations of assessing patients with neurologic injury. Artificial intelligence (AI) has garnered interest in the basic mana...
Nervous System Diseases Machine Learning Critical Care Disease Management Humans Artificial Intelligence
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Machine learning and blood pressure.

Journal of clinical hypertension (Greenwich, Conn.) Sep 19, 2019
Machine learning (ML) is a type of artificial intelligence (AI) based on pattern recognition. There are different forms of supervised and unsupervised learning algorithms that are being used to identify and predict blood pressure (BP) and other measu...
Risk Assessment Humans Artificial Intelligence Hypertension Machine Learning Disease Management Prognosis Cardiovascular Diseases
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Artificial Intelligence Methods for Surgical Site Infection: Impacts on Detection, Monitoring, and Decision Making.

Surgical infections Aug 27, 2019
There has been tremendous growth in the amount of new surgical site infection (SSI) data generated. Key challenges exist in understanding the data for robust clinical decision-support. Limitations of traditional methodologies to handle these data le...
Epidemiological Monitoring Decision Making Surgical Wound Infection Humans Disease Management Artificial Intelligence Image Processing, Computer-Assisted
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