AIMC Topic: ROC Curve

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A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections.

Nature communications Mar 4, 2020
Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral ...
Host-Pathogen Interactions Virus Diseases Support Vector Machine Bacterial Infections Acute Disease Datasets as Topic Aged, 80 and over Adult Male Middle Aged Gene Expression Profiling Sepsis Hospital Mortality ROC Curve RNA, Messenger Humans Neural Networks, Computer Intensive Care Units Female Aged
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Predicting Wait Times in Pediatric Ophthalmology Outpatient Clinic Using Machine Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Patient perceptions of wait time during outpatient office visits can affect patient satisfaction. Providing accurate information about wait times could improve patients' satisfaction by reducing uncertainty. However, these are rarely known about effi...
Patient Satisfaction Models, Statistical Humans Linear Models Ophthalmology Machine Learning Time-to-Treatment Supervised Machine Learning Time Factors Ambulatory Care Facilities ROC Curve Pediatrics
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Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, which commonly goes undetected. Continuous glucose monitoring (CGM) devices have enabled prediction of impending nocturnal hypoglycemia, however, prior efforts have been li...
Blood Glucose Self-Monitoring Monitoring, Ambulatory Hypoglycemic Agents Humans Area Under Curve Machine Learning Models, Biological Blood Glucose Hypoglycemia Insulin ROC Curve Diabetes Mellitus, Type 1
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Assessing Contribution of Higher Order Clinical Risk Factors to Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
The goal of this study was to investigate the application of machine learning models capable of capturing multiplica tive and temporal clinical risk factors for outcome prediction inpatients with aneurysmal subarachnoid hemorrhage (aSAH). We examined...
Humans Neural Networks, Computer Logistic Models Machine Learning Prognosis Treatment Outcome Risk Factors Area Under Curve Cohort Studies Subarachnoid Hemorrhage ROC Curve
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Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data. It is an effective technique for collecting a majority of the cervical cells and aiding cytopathologists in locating abnormal cells. Mos...
ROC Curve Humans Papanicolaou Test Vaginal Smears Neural Networks, Computer Cervix Uteri Female Datasets as Topic Uterine Cervical Neoplasms Deep Learning
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Deep learning computer vision algorithm for detecting kidney stone composition.

BJU international Mar 3, 2020
OBJECTIVES: To assess the recall of a deep learning (DL) method to automatically detect kidney stones composition from digital photographs of stones.
Humans Algorithms Deep Learning Photography Cystine Calcium Oxalate Struvite ROC Curve Uric Acid Calcium Phosphates Image Interpretation, Computer-Assisted Kidney Calculi
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Machine-learning-based computed tomography radiomic analysis for histologic subtype classification of thymic epithelial tumours.

European journal of radiology Mar 2, 2020
PURPOSE: To evaluate the performance of machine-learning-based computed tomography (CT) radiomic analysis to differentiate high-risk thymic epithelial tumours (TETs) from low-risk TETs according to the WHO classification.
Retrospective Studies Image Interpretation, Computer-Assisted Humans Tomography, X-Ray Computed Neoplasms, Glandular and Epithelial Machine Learning Female Thymus Neoplasms Adult Aged Thymus Gland Male Young Adult Middle Aged ROC Curve Area Under Curve
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CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.

European radiology Feb 28, 2020
PURPOSE: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography ...
Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Neoplasm Invasiveness Female Neoplasm Recurrence, Local Risk Factors ROC Curve Adenocarcinoma of Lung Sensitivity and Specificity Image Processing, Computer-Assisted Male Lung Neoplasms Middle Aged Logistic Models
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Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network.

Urolithiasis Feb 27, 2020
The objectives were to develop and validate a Convolutional Neural Network (CNN) using local features for differentiating distal ureteral stones from pelvic phleboliths, compare the CNN method with a semi-quantitative method and with radiologists' as...
Humans Ureteral Calculi Renal Colic Aged Neural Networks, Computer Aged, 80 and over Middle Aged Retrospective Studies Adolescent Young Adult ROC Curve Pelvis Tomography, X-Ray Computed Adult Diagnosis, Differential Datasets as Topic Female Male Vascular Calcification Ureter Veins
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Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier.

The British journal of ophthalmology Feb 25, 2020
BACKGROUND/AIMS: To assess the performance of a deep learning classifier for differentiation of glaucomatous optic neuropathy (GON) from compressive optic neuropathy (CON) based on ganglion cell-inner plexiform layer (GCIPL) and retinal nerve fibre l...
Retrospective Studies Female Aged Intraocular Pressure Male Humans Glaucoma Nerve Fibers Optic Disk Retinal Ganglion Cells Middle Aged Visual Fields Deep Learning Optic Nerve Diseases ROC Curve Follow-Up Studies Tomography, Optical Coherence
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