AIMC Topic: ROC Curve

Clear Filters Showing 1471 to 1480 of 3402 articles

Predicting self-intercepted medication ordering errors using machine learning.

PloS one Jul 14, 2021
Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to mis...
Infant Adolescent Child, Preschool Child Young Adult Adult Male Middle Aged ROC Curve Female Humans Logistic Models Aged Machine Learning Medication Errors Aged, 80 and over Medical Order Entry Systems
View on PubMed DOI

Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver Jul 9, 2021
BACKGROUND: Patients with Crohn's disease (CD) frequently undergo abdominopelvic computed tomography (APCT) in the emergency department (ED). It's essential to diagnose clinically actionable findings (CAF) as they may need immediate intervention, fre...
ROC Curve Emergency Service, Hospital Humans Crohn Disease Male Artificial Intelligence Patient Selection Retrospective Studies Middle Aged Tomography, X-Ray Computed Risk Assessment Young Adult Adolescent Adult
View on PubMed DOI

CRP (C-Reactive Protein) in Outcome Prediction After Subarachnoid Hemorrhage and the Role of Machine Learning.

Stroke Jul 9, 2021
BACKGROUND AND PURPOSE: Outcome prediction after aneurysmal subarachnoid hemorrhage (aSAH) is challenging. CRP (C-reactive protein) has been reported to be associated with outcome, but it is unclear if this is independent of other predictors and appl...
C-Reactive Protein Subarachnoid Hemorrhage Treatment Outcome Support Vector Machine Models, Statistical Predictive Value of Tests Female Reproducibility of Results Aged Young Adult Humans Machine Learning Adult Middle Aged Prognosis Male ROC Curve Logistic Models
View on PubMed DOI

A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.

Nature communications Jul 9, 2021
Deep learning algorithms trained on instances that violate the assumption of being independent and identically distributed (i.i.d.) are known to experience destructive interference, a phenomenon characterized by a degradation in performance. Such a v...
Deep Learning Time Factors ROC Curve Humans Clinical Decision-Making Decision Support Systems, Clinical Datasets as Topic Electrocardiography Models, Cardiovascular Seasons Arrhythmias, Cardiac
View on PubMed DOI

4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection.

IEEE transactions on neural networks and learning systems Jul 6, 2021
Due to the high availability of large-scale annotated image datasets, knowledge transfer from pretrained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with da...
Humans Neural Networks, Computer Artificial Intelligence Machine Learning ROC Curve Knowledge Bases Image Interpretation, Computer-Assisted Thorax X-Rays COVID-19 Algorithms Reproducibility of Results Deep Learning
View on PubMed DOI

Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies.

BioMed research international Jul 5, 2021
The purpose of this study was to propose a machine learning model and assess its ability to classify TMJ pathologies on magnetic resonance (MR) images. This retrospective cohort study included 214 TMJs from 107 patients with TMJ signs and symptoms. A...
Aged Middle Aged Humans Young Adult ROC Curve Machine Learning Adult Magnetic Resonance Imaging Algorithms Male Temporomandibular Joint Disorders Female
View on PubMed DOI

Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches).

Korean journal of radiology Jul 1, 2021
The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness...
Humans ROC Curve Deep Learning
View on PubMed DOI

Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study.

The Lancet. Digital health Jul 1, 2021
BACKGROUND: Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray interpretati...
Artificial Intelligence Thoracic Injuries Retrospective Studies Female Humans Male Middle Aged Deep Learning Adolescent ROC Curve Aged Aged, 80 and over Young Adult Adult X-Rays Radiographic Image Interpretation, Computer-Assisted Thoracic Neoplasms Models, Biological Mass Screening Radiography, Thoracic Area Under Curve Infections Radiologists
View on PubMed DOI

In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.

Molecular diversity Jul 1, 2021
Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focu...
Cheminformatics Drug-Related Side Effects and Adverse Reactions Databases, Chemical Blood Cells Quantitative Structure-Activity Relationship Humans Machine Learning Algorithms Reproducibility of Results Deep Learning Drug Discovery ROC Curve Models, Molecular
View on PubMed DOI

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer.

JAMA network open Jul 1, 2021
IMPORTANCE: Electronic health records (EHRs) provide a low-cost means of accessing detailed longitudinal clinical data for large populations. A lung cancer cohort assembled from EHR data would be a powerful platform for clinical outcome studies.
Survival Analysis Survivors Boston Algorithms Prognosis Electronic Health Records Female ROC Curve Humans Lung Neoplasms Machine Learning Area Under Curve Male Cohort Studies
View on PubMed DOI
Popular Topics
Recent Journals