AIMC Topic: ROC Curve

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Predicting Corticosteroid-Free Biologic Remission with Vedolizumab in Crohn's Disease.

Inflammatory bowel diseases May 18, 2018
BACKGROUND AND AIMS: Vedolizumab (VDZ) is effective for Crohn's disease (CD) but costly and is slow to produce remission. Early knowledge of whether vedolizumab is likely to succeed is valuable for physicians, patients, and insurers.
Remission Induction Adrenal Cortex Hormones C-Reactive Protein Area Under Curve Crohn Disease Gastrointestinal Agents Female Young Adult Adult Male Middle Aged Cohort Studies ROC Curve Treatment Outcome Antibodies, Monoclonal, Humanized Biological Products Humans Machine Learning
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Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network.

IEEE transactions on medical imaging May 1, 2018
Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individua...
Humans Neural Networks, Computer Prostatic Neoplasms Prostate Image Interpretation, Computer-Assisted Algorithms Male ROC Curve Magnetic Resonance Imaging
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A Preventive Model for Muscle Injuries: A Novel Approach based on Learning Algorithms.

Medicine and science in sports and exercise May 1, 2018
INTRODUCTION: The application of contemporary statistical approaches coming from Machine Learning and Data Mining environments to build more robust predictive models to identify athletes at high risk for injury might support injury prevention strateg...
Humans Machine Learning Algorithms Lower Extremity Decision Making Muscle, Skeletal Athletes Athletic Injuries Prospective Studies Risk Factors Male ROC Curve Models, Theoretical
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[Application of support vector machine in predicting in-hospital mortality risk of patients with acute kidney injury in ICU].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences Apr 18, 2018
OBJECTIVE: To construct an in-hospital mortality prediction model for patients with acute kidney injury (AKI) in intensive care unit (ICU) by using support vector machine (SVM), and compare it with the simplified acute physiology score II (SAPS-II) w...
Sensitivity and Specificity Hospital Mortality ROC Curve Support Vector Machine Critical Care Humans Prognosis Acute Kidney Injury Intensive Care Units
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An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Critical care medicine Apr 1, 2018
OBJECTIVES: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-ti...
Blood Pressure Heart Rate Sex Factors Academic Medical Centers Time-to-Treatment Organ Dysfunction Scores Vital Signs ROC Curve Decision Support Systems, Clinical Socioeconomic Factors Electrocardiography Age Factors Severity of Illness Index Sepsis Time Factors Hospital Mortality Humans Machine Learning Critical Illness Intensive Care Units Electronic Health Records Female Aged Male Middle Aged Comorbidity
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Artificial Neural Networking Model for the Prediction of Early Occlusion of Bilateral Plastic Stent Placement for Inoperable Hilar Cholangiocarcinoma.

Surgical laparoscopy, endoscopy & percutaneous techniques Apr 1, 2018
BACKGROUND: This study aimed to determine whether the back-propagation artificial neural network (BP-ANN) model could be constructed to accurately in predicting early occlusion of bilateral plastic stent placement for inoperable hilar cholangiocarcin...
Postoperative Complications ROC Curve Follow-Up Studies Plastics Humans Neoplasm Staging Neural Networks, Computer Retrospective Studies Prospective Studies Female Aged Male Klatskin Tumor Prosthesis Failure Prosthesis Design Bile Ducts, Intrahepatic Stents Early Diagnosis Palliative Care Bile Duct Neoplasms
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Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease Mar 1, 2018
OBJECTIVE: To evaluate the feasibility of Deep Learning-based detection and classification of pathological patterns in a set of digital photographs of chest X-ray (CXR) images of tuberculosis (TB) patients.
Deep Learning HIV Infections Uganda ROC Curve Predictive Value of Tests Coinfection Feasibility Studies Tuberculosis, Pulmonary Radiography, Thoracic Teleradiology Software Prospective Studies Female Adult Male Humans
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Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning.

Cell Feb 22, 2018
The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common t...
Humans ROC Curve Neural Networks, Computer Pneumonia Diagnostic Imaging Tomography, Optical Coherence Reproducibility of Results Deep Learning Child
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Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society Feb 1, 2018
In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in España [MADR-E]). The aim is to ...
Female Reproducibility of Results Liver Diseases Risk Factors Donor Selection Adult London Humans Male Graft Survival Middle Aged Neural Networks, Computer Treatment Outcome Risk Assessment Algorithms Area Under Curve Time Factors Decision Support Techniques Liver Transplantation ROC Curve Computer Simulation Waiting Lists Tissue Donors
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Utilising Information of the Case Fee Catalogue to Enhance 30-Day Readmission Prediction in the German DRG System.

Studies in health technology and informatics Jan 1, 2018
Unplanned hospital readmissions are a burden to the healthcare system and to the patients. To lower the readmission rates, machine learning approaches can be used to create predictive models, with the intention to provide actionable information for c...
Hospitals Humans ROC Curve Machine Learning Patient Readmission Forecasting Germany Prognosis Diagnosis-Related Groups
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