AIMC Topic: ROC Curve

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Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs.

Journal of healthcare engineering Aug 24, 2020
The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple cat...
ROC Curve Area Under Curve Equipment Design Radiographic Image Interpretation, Computer-Assisted Tuberculosis, Pulmonary False Positive Reactions Radiography, Thoracic Thorax X-Rays Reproducibility of Results Sensitivity and Specificity Diagnosis, Computer-Assisted Deep Learning Computer Systems Humans Neural Networks, Computer Algorithms Databases, Factual
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Automated detection of early-stage ROP using a deep convolutional neural network.

The British journal of ophthalmology Aug 23, 2020
BACKGROUND/AIM: To automatically detect and classify the early stages of retinopathy of prematurity (ROP) using a deep convolutional neural network (CNN).
Neural Networks, Computer Algorithms Retrospective Studies Female Infant, Newborn Sensitivity and Specificity Cross-Sectional Studies Male Diagnosis, Computer-Assisted ROC Curve Humans Gestational Age Retinopathy of Prematurity Image Processing, Computer-Assisted Birth Weight Infant, Premature Infant, Low Birth Weight
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Machine learning models predicting multidrug resistant urinary tract infections using "DsaaS".

BMC bioinformatics Aug 21, 2020
BACKGROUND: The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine L...
Area Under Curve Italy Urinary Tract Infections Models, Biological Middle Aged Drug Resistance, Multiple, Bacterial ROC Curve Support Vector Machine Machine Learning Algorithms Female Aged Male Humans Neural Networks, Computer
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Identifying disease trajectories with predicate information from a knowledge graph.

Journal of biomedical semantics Aug 20, 2020
BACKGROUND: Knowledge graphs can represent the contents of biomedical literature and databases as subject-predicate-object triples, thereby enabling comprehensive analyses that identify e.g. relationships between diseases. Some diseases are often dia...
Humans ROC Curve Information Storage and Retrieval Semantics Biological Ontologies Disease Computer Graphics
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Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study.

The European respiratory journal Aug 20, 2020
BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality.
ROC Curve Decision Support Systems, Clinical Age Factors Predictive Value of Tests Triage Risk Assessment Severity of Illness Index China Cohort Studies Hospital Mortality Survival Analysis Internationality Belgium Clinical Laboratory Techniques Pneumonia, Viral Area Under Curve Italy Coronavirus Infections COVID-19 Testing Sex Factors Pandemics COVID-19 Retrospective Studies Female Reproducibility of Results Hospitalization Aged Adult Male Middle Aged Humans Machine Learning
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The FUTUREPAIN study: Validating a questionnaire to predict the probability of having chronic pain 7-10 years into the future.

PloS one Aug 20, 2020
OBJECTIVES: The FUTUREPAIN study develops a short general-purpose questionnaire, based on the biopsychosocial model, to predict the probability of developing or maintaining moderate-to-severe chronic pain 7-10 years into the future.
Aged Aged, 80 and over Adult Male Middle Aged Machine Learning Algorithms Retrospective Studies Female Surveys and Questionnaires ROC Curve Chronic Pain Humans
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Predicting essential genes of 41 prokaryotes by a semi-supervised method.

Analytical biochemistry Aug 19, 2020
Essential genes are vitally important to the survival and reproduction of organisms. Many machine learning methods have been widely employed to predict essential genes and have obtained satisfactory results. However, most of these methods are supervi...
Area Under Curve Supervised Machine Learning Prokaryotic Cells ROC Curve Databases, Genetic Genes, Essential
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Using Machine Learning to Make Predictions in Patients Who Fall.

The Journal of surgical research Aug 18, 2020
BACKGROUND: As the population ages, the incidence of traumatic falls has been increasing. We hypothesize that a machine learning algorithm can more accurately predict mortality after a fall compared with a standard logistic regression (LR) model base...
Injury Severity Score Trauma Centers Glasgow Coma Scale Length of Stay Accidental Falls ROC Curve Decision Trees Patient Discharge Aged, 80 and over Adult Male Middle Aged Humans Machine Learning Algorithms Retrospective Studies Intensive Care Units Female Aged
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Utilization of machine-learning models to accurately predict the risk for critical COVID-19.

Internal and emergency medicine Aug 18, 2020
Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 base...
Retrospective Studies Critical Illness Female Hospitalization Aged Humans Machine Learning Pandemics COVID-19 ROC Curve Coronavirus Infections APACHE Pneumonia, Viral Aged, 80 and over Adult Male Middle Aged Risk Assessment
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m7GPredictor: An improved machine learning-based model for predicting internal m7G modifications using sequence properties.

Analytical biochemistry Aug 14, 2020
As one of the most important post-transcriptional modifications, the N7-methylguanosine (m7G) plays a key role in many RNA processing events. The accurate identification of m7G is crucial for elucidating its biological significance and future applica...
Machine Learning Databases, Genetic Area Under Curve Guanosine User-Computer Interface ROC Curve Models, Theoretical Support Vector Machine
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