AIMC Topic: Predictive Value of Tests
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Impact of ECG Characteristics on the Performance of an Artificial Intelligence Enabled ECG for Predicting Left Ventricular Dysfunction.
Circulation. Arrhythmia and electrophysiology
May 17, 2021
Hopes and Hypes for Artificial Intelligence in Colorectal Cancer Screening.
Gastroenterology
May 11, 2021
Assistance from Automated ASPECTS Software Improves Reader Performance.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
May 11, 2021
PURPOSE: To compare physicians' ability to read Alberta Stroke Program Early CT Score (ASPECTS) in patients with a large vessel occlusion within 6 hours of symptom onset when assisted by a machine learning-based automatic software tool, compared with...
Aged
Automation
Male
Middle Cerebral Artery
Middle Aged
Carotid Artery, Internal
Clinical Competence
Neurologists
Software
Magnetic Resonance Imaging
Humans
Prognosis
Predictive Value of Tests
Tomography, X-Ray Computed
Machine Learning
Female
Ischemic Stroke
Reproducibility of Results
Radiologists
Radiographic Image Interpretation, Computer-Assisted
Observer Variation
Predicting Infective Endocarditis After Transcatheter Aortic Valve Implantation Via a Risk Model.
The American journal of cardiology
May 9, 2021
Predicting Infarct Core From Computed Tomography Perfusion in Acute Ischemia With Machine Learning: Lessons From the ISLES Challenge.
Stroke
May 7, 2021
BACKGROUND AND PURPOSE: The ISLES challenge (Ischemic Stroke Lesion Segmentation) enables globally diverse teams to compete to develop advanced tools for stroke lesion analysis with machine learning. Detection of irreversibly damaged tissue on comput...
Altered Functional Connectivity of the Primary Visual Cortex in Patients With Iridocyclitis and Assessment of Its Predictive Value Using Machine Learning.
Frontiers in immunology
May 7, 2021
PURPOSE: To explore the intrinsic functional connectivity (FC) alteration of the primary visual cortex (V1) between individuals with iridocyclitis and healthy controls (HCs) by the resting-state functional magnetic resonance imaging (fMRI) technique,...
Deep Learning for Fully Automated Prediction of Overall Survival in Patients with Oropharyngeal Cancer Using FDG-PET Imaging.
Clinical cancer research : an official journal of the American Association for Cancer Research
May 4, 2021
PURPOSE: Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning-based fully-automated tool called the DeepPET-OPSCC biomarker for predicting ove...
Feature importance of machine learning prediction models shows structurally active part and important physicochemical features in drug design.
Drug metabolism and pharmacokinetics
May 3, 2021
The objective of this study was to obtain the indicators of physicochemical parameters and structurally active sites to design new chemical entities with desirable pharmacokinetic profiles by investigating the process by which machine learning predic...
Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.
JAMA network open
May 3, 2021
IMPORTANCE: Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) is limited by existing risk prediction models. Machine learning algorithms may result in superior pre...
Myocardial Ischemia
Clinical Decision-Making
Thienopyridines
Female
Aged
Aged, 80 and over
Anticoagulants
Young Adult
Atrial Fibrillation
Adolescent
Venous Thromboembolism
Humans
Cohort Studies
Antifibrinolytic Agents
Machine Learning
United States
Adult
Gastrointestinal Hemorrhage
Retrospective Studies
Predictive Value of Tests
Cross-Sectional Studies
Fibrinolytic Agents
Male
Middle Aged
Risk Assessment
Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
Clinical radiology
May 1, 2021
AIM: To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progression (tPD) and pseudoprogression (psPD) in patients with glioblastoma.
Adult
Sensitivity and Specificity
Brain Neoplasms
Male
Middle Aged
Retrospective Studies
Female
Reproducibility of Results
Aged
Young Adult
Humans
Predictive Value of Tests
Machine Learning
Glioblastoma
Brain
Adolescent
Image Interpretation, Computer-Assisted
Image Enhancement
Treatment Outcome
Chemoradiotherapy
Contrast Media
Magnetic Resonance Imaging
Diagnosis, Differential
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