AIMC Topic: ROC Curve

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Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2019
OBJECTIVE: Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised ma...
Humans Machine Learning Electronic Health Records Female ROC Curve Natural Language Processing Alcoholism Wounds and Injuries Trauma Centers Adult Male Middle Aged Cohort Studies
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Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions.

Journal of the American Heart Association Feb 19, 2019
Background An angiography-based supervised machine learning ( ML ) algorithm was developed to classify lesions as having fractional flow reserve ≤0.80 versus >0.80. Methods and Results With a 4:1 ratio, 1501 patients with 1501 intermediate lesions we...
Coronary Stenosis Fractional Flow Reserve, Myocardial Imaging, Three-Dimensional Retrospective Studies Humans Severity of Illness Index Coronary Angiography Female Machine Learning ROC Curve Male Coronary Vessels Algorithms Middle Aged
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Low coherence quantitative phase microscopy with machine learning model and Raman spectroscopy for the study of breast cancer cells and their classification.

Applied optics Feb 10, 2019
Early-stage detection of breast cancer is the primary requirement in modern healthcare as it is the most common cancer among women worldwide. Histopathology is the most widely preferred method for the diagnosis of breast cancer, but it requires long ...
Interferometry Breast Neoplasms ROC Curve Spectrum Analysis, Raman Microscopy, Interference Machine Learning Female Reproducibility of Results Humans Sensitivity and Specificity
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Deep Learning for Prediction of AMD Progression: A Pilot Study.

Investigative ophthalmology & visual science Feb 1, 2019
PURPOSE: To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degeneration (AMD) using optical coherence tomography (OCT) imaging and methods of deep learning.
Female Aged Aged, 80 and over Male Middle Aged Disease Progression Diagnosis, Computer-Assisted Deep Learning Humans Neural Networks, Computer Machine Learning Wet Macular Degeneration Pilot Projects ROC Curve Follow-Up Studies Tomography, Optical Coherence
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Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Medicine Feb 1, 2019
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratifi...
Organ Dysfunction Scores Triage Emergency Service, Hospital Singapore Heart Rate Male Middle Aged Severity of Illness Index Sepsis Hospital Mortality Systemic Inflammatory Response Syndrome ROC Curve Socioeconomic Factors Female Reproducibility of Results Aged Aged, 80 and over Humans Machine Learning
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Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks.

Yonsei medical journal Feb 1, 2019
PURPOSE: Many studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, such as user convenience and reproducibility. The purpose of this study was to develop a T2DM predic...
Humans Machine Learning Algorithms Male Cardiovascular Diseases Middle Aged ROC Curve Diabetes Mellitus, Type 2 Republic of Korea Female Reproducibility of Results Risk Factors Logistic Models Follow-Up Studies
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Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.

Investigative radiology Feb 1, 2019
PURPOSE: The aim of this study was to assess the potential of machine learning with multiparametric magnetic resonance imaging (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival ...
Breast Survival Analysis Chemotherapy, Adjuvant Diffusion Magnetic Resonance Imaging Adult Prospective Studies Breast Neoplasms Female Middle Aged Reproducibility of Results Humans ROC Curve Treatment Outcome Aged Machine Learning Neoadjuvant Therapy Magnetic Resonance Imaging Predictive Value of Tests
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Machine learning approach to literature mining for the genetics of complex diseases.

Database : the journal of biological databases and curation Jan 1, 2019
To generate a parsimonious gene set for understanding the mechanisms underlying complex diseases, we reasoned it was necessary to combine the curation of public literature, review of experimental databases and interpolation of pathway-associated gene...
Databases, Genetic Humans Area Under Curve Machine Learning Data Mining ROC Curve Disease Models, Theoretical
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Automatic prostate segmentation based on fusion between deep network and variational methods.

Journal of X-ray science and technology Jan 1, 2019
BACKGROUND: Segmentation of prostate from magnetic resonance images (MRI) is a critical process for guiding prostate puncture and biopsy. Currently, the best results are obtained by Convolutional Neural Network (CNN). However, challenges still exist ...
Image-Guided Biopsy Humans Imaging, Three-Dimensional Neural Networks, Computer Image Processing, Computer-Assisted Male Magnetic Resonance Imaging ROC Curve Prostate
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Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning.

Journal of X-ray science and technology Jan 1, 2019
BACKGROUND: Deep learning has made spectacular achievements in analysing natural images, but it faces challenges for medical applications partly due to inadequate images.
Reproducibility of Results Lung Deep Learning ROC Curve Humans Databases, Factual Tomography, X-Ray Computed Lung Neoplasms Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted
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