AIMC Topic: ROC Curve

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Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

International journal of computer assisted radiology and surgery Jun 16, 2020
PURPOSE: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance imag...
Area Under Curve Chemotherapy, Adjuvant Image Processing, Computer-Assisted Treatment Outcome Neoadjuvant Therapy Contrast Media Magnetic Resonance Imaging Neural Networks, Computer Retrospective Studies Female Adult Breast Neoplasms Middle Aged Antineoplastic Agents Diagnosis, Computer-Assisted Deep Learning ROC Curve Humans
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Predicting dengue importation into Europe, using machine learning and model-agnostic methods.

Scientific reports Jun 16, 2020
The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and i...
Humans Machine Learning Algorithms Epidemics Travel Dengue Forecasting ROC Curve Europe Models, Statistical Aircraft
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Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesions with dual validation.

European radiology Jun 10, 2020
OBJECTIVE: To evaluate machine learning-based classifiers in detecting clinically significant prostate cancer (PCa) with Prostate Imaging Reporting and Data System (PI-RADS) score 3 lesions.
Humans Machine Learning Multiparametric Magnetic Resonance Imaging Support Vector Machine Retrospective Studies Prostate-Specific Antigen Male Reproducibility of Results Decision Trees Prostate Middle Aged Validation Studies as Topic Area Under Curve Aged Ultrasonography Image-Guided Biopsy Aged, 80 and over Radiography ROC Curve Unnecessary Procedures Prostatic Neoplasms Logistic Models
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The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty.

Journal of shoulder and elbow surgery Jun 9, 2020
HYPOTHESIS/PURPOSE: The objective is to develop and validate an artificial intelligence model, specifically an artificial neural network (ANN), to predict length of stay (LOS), discharge disposition, and inpatient charges for primary anatomic total (...
Hospital Charges Arthroplasty, Replacement, Shoulder Hemiarthroplasty Shoulder Injuries Databases, Factual ROC Curve Female Models, Statistical Reproducibility of Results Humans Patient Discharge Aged Neural Networks, Computer Male Osteoarthritis Aged, 80 and over Machine Learning Middle Aged Hospital Costs Forecasting Postoperative Complications Length of Stay
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Modest Clostridiodes difficile infection prediction using machine learning models in a tertiary care hospital.

Diagnostic microbiology and infectious disease Jun 8, 2020
Previous studies have shown promising results of machine learning (ML) models for predicting health outcomes. We develop and test ML models for predicting Clostridioides difficile infection (CDI) in hospitalized patients. This is a retrospective coho...
Bayes Theorem Tertiary Care Centers Humans Clostridium Infections Machine Learning Clostridioides difficile Forecasting Retrospective Studies Anti-Bacterial Agents Diarrhea Female ROC Curve Aged Logistic Models Male Area Under Curve Middle Aged
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An improved clear cell renal cell carcinoma stage prediction model based on gene sets.

BMC bioinformatics Jun 8, 2020
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Thus, developing an efficient s...
Logistic Models Area Under Curve Neoplasm Staging RNA Kidney Neoplasms Carcinoma, Renal Cell Humans Machine Learning Female ROC Curve
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Prospective Analysis Using a Novel CNN Algorithm to Distinguish Atypical Ductal Hyperplasia From Ductal Carcinoma in Situ in Breast.

Clinical breast cancer Jun 7, 2020
INTRODUCTION: We previously developed a convolutional neural networks (CNN)-based algorithm to distinguish atypical ductal hyperplasia (ADH) from ductal carcinoma in situ (DCIS) using a mammographic dataset. The purpose of this study is to further va...
Humans Neural Networks, Computer Prospective Studies Female Aged Adult Mammary Glands, Human Breast Neoplasms Biopsy Middle Aged Radiographic Image Interpretation, Computer-Assisted ROC Curve Carcinoma, Intraductal, Noninfiltrating Mammography Datasets as Topic Diagnosis, Differential Hyperplasia
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Matrix factorization with neural network for predicting circRNA-RBP interactions.

BMC bioinformatics Jun 5, 2020
BACKGROUND: Circular RNA (circRNA) has been extensively identified in cells and tissues, and plays crucial roles in human diseases and biological processes. circRNA could act as dynamic scaffolding molecules that modulate protein-protein interactions...
Cell Line, Tumor ROC Curve RNA-Binding Proteins Area Under Curve RNA, Circular Humans Neural Networks, Computer Databases, Factual Neoplasms
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Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients.

PloS one Jun 5, 2020
Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient's well-being and for successful management of the condition. In thi...
ROC Curve Renal Insufficiency, Chronic Taiwan Models, Biological Clinical Decision-Making Humans Datasets as Topic Time-to-Treatment Disease Progression Machine Learning Comorbidity Retrospective Studies Severity of Illness Index Renal Replacement Therapy Time Factors
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Identification of feature risk pathways of smoking-induced lung cancer based on SVM.

PloS one Jun 4, 2020
OBJECTIVE: The present study aims to explore the role of smoking factors in the risk of lung cancer and screen the feature risk pathways of smoking-induced lung cancer.
Area Under Curve High-Throughput Nucleotide Sequencing Smoking DNA, Neoplasm Gene Ontology Humans Lung Neoplasms Risk Factors Signal Transduction Protein Interaction Mapping Gene Expression Profiling Support Vector Machine ROC Curve Databases, Genetic Cluster Analysis
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