AIMC Topic: Predictive Value of Tests

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Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry.

JACC. Cardiovascular imaging Aug 14, 2019
OBJECTIVES: This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning-based coronary computed tomography (CT) angiography (cCTA)-derived fractional flow reserve (CT-...
Fractional Flow Reserve, Myocardial North America Registries Humans Coronary Artery Disease Machine Learning Vascular Calcification Female Reproducibility of Results Asia Aged Computed Tomography Angiography Male Middle Aged Severity of Illness Index Diagnosis, Computer-Assisted Europe Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted
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Predicting Clinical Outcomes of Large Vessel Occlusion Before Mechanical Thrombectomy Using Machine Learning.

Stroke Aug 14, 2019
Background and Purpose- The clinical course of acute ischemic stroke with large vessel occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to model this process with machine learning and predict the long-term clinica...
Humans Machine Learning Retrospective Studies Female Aged Aged, 80 and over Male Cerebrovascular Disorders Thrombectomy Middle Aged Cohort Studies Treatment Outcome Predictive Value of Tests
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Transfer learning for neural network model in chlorophyll-a dynamics prediction.

Environmental science and pollution research international Aug 13, 2019
Neural network models have been used to predict chlorophyll-a concentration dynamics. However, as model generalization ability decreases, (i) the performance of the models gradually decreases over time; (ii) the accuracy and performance of the models...
China Predictive Value of Tests Kinetics Chlorophyll A Neural Networks, Computer Machine Learning Eutrophication
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Home care aides' observations and machine learning algorithms for the prediction of visits to emergency departments by older community-dwelling individuals receiving home care assistance: A proof of concept study.

PloS one Aug 13, 2019
BACKGROUND: Older individuals receiving home assistance are at high risk for emergency visits and unplanned hospitalization. Anticipating their health difficulties could prevent these events. This study investigated the effectiveness of an at-home mo...
Frail Elderly Hospitalization Predictive Value of Tests Humans Independent Living Aged Emergency Service, Hospital Home Care Services Machine Learning Aged, 80 and over Geriatric Assessment Home Health Aides Male Algorithms Proof of Concept Study Surveys and Questionnaires Prospective Studies Female
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Neural-network classification of cardiac disease from P cardiovascular magnetic resonance spectroscopy measures of creatine kinase energy metabolism.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Aug 12, 2019
BACKGROUND: The heart's energy demand per gram of tissue is the body's highest and creatine kinase (CK) metabolism, its primary energy reserve, is compromised in common heart diseases. Here, neural-network analysis is used to test whether noninvasive...
Heart Diseases Predictive Value of Tests Energy Metabolism Biomarkers Myocardium Magnetic Resonance Spectroscopy Female Young Adult Creatine Kinase Phosphocreatine Reproducibility of Results Humans Adult Kinetics Phosphorus Isotopes Aged Neural Networks, Computer Male Adenosine Triphosphate Aged, 80 and over Machine Learning Middle Aged Severity of Illness Index
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Machine learning to predict cardiovascular risk.

International journal of clinical practice Aug 4, 2019
AIMS: To analyse the predictive capacity of 15 machine learning methods for estimating cardiovascular risk in a cohort and to compare them with other risk scales.
Humans Machine Learning Algorithms Female Follow-Up Studies Predictive Value of Tests Area Under Curve Bayes Theorem Spain Risk Factors Male Cardiovascular Diseases Middle Aged Cohort Studies
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Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty.

Journal of shoulder and elbow surgery Aug 3, 2019
BACKGROUND: We aimed to demonstrate that supervised machine learning (ML) models can better predict postoperative complications after total shoulder arthroplasty (TSA) than comorbidity indices.
Predictive Value of Tests Supervised Machine Learning Hematocrit Patient Readmission Smoking ROC Curve Body Mass Index Age Factors Blood Transfusion Operative Time Reoperation Serum Albumin Arthroplasty, Replacement, Shoulder Humans Forecasting Databases, Factual Female Aged Male Middle Aged Postoperative Complications Comorbidity Length of Stay
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Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.

Neuroradiology Aug 2, 2019
PURPOSE: Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker whic...
Ki-67 Antigen Image Processing, Computer-Assisted Algorithms Magnetic Resonance Imaging Female Pituitary Neoplasms Reproducibility of Results Adenoma Aged Predictive Value of Tests Aged, 80 and over Adult Biomarkers, Tumor Humans Male Machine Learning Middle Aged Adolescent
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An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction.

Lancet (London, England) Aug 1, 2019
BACKGROUND: Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to deve...
Algorithms Retrospective Studies Female Aged Adult Humans Neural Networks, Computer Predictive Value of Tests Male Atrial Fibrillation Middle Aged Atrial Flutter Case-Control Studies ROC Curve Electrocardiography
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Predicting death by suicide using administrative health care system data: Can feedforward neural network models improve upon logistic regression models?

Journal of affective disorders Jul 30, 2019
BACKGROUND: Suicide is a leading cause of death worldwide. With the increasing volume of administrative health care data, there is an opportunity to evaluate whether machine learning models can improve upon statistical models for quantifying suicide ...
Logistic Models Predictive Value of Tests Machine Learning Female Male Risk Assessment Suicide Humans Neural Networks, Computer
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