AIMC Topic:
ROC Curve

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Effective DNA binding protein prediction by using key features via Chou's general PseAAC.

Journal of theoretical biology Oct 11, 2018
DNA-binding proteins (DBPs) are responsible for several cellular functions, starting from our immunity system to the transport of oxygen. In the recent studies, scientists have used supervised machine learning based methods that use information from ...
Algorithms Reproducibility of Results ROC Curve Computational Biology Support Vector Machine DNA-Binding Proteins Amino Acid Sequence
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Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening.

Clinical cancer research : an official journal of the American Association for Cancer Research Oct 11, 2018
PURPOSE: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish...
Mass Screening Early Detection of Cancer Public Health Surveillance Breast Neoplasms Humans Mammography Image Interpretation, Computer-Assisted Deep Learning Retrospective Studies ROC Curve Female Image Processing, Computer-Assisted Reproducibility of Results
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3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects.

Journal of magnetic resonance imaging : JMRI Oct 10, 2018
BACKGROUND: Semiquantitative assessment of MRI plays a central role in musculoskeletal research; however, in the clinical setting MRI reports often tend to be subjective and qualitative. Grading schemes utilized in research are not used because they ...
Meniscus Imaging, Three-Dimensional Magnetic Resonance Imaging False Positive Reactions Osteoarthritis, Knee Cartilage, Articular Anterior Cruciate Ligament Injuries Anterior Cruciate Ligament Anterior Cruciate Ligament Reconstruction Male Middle Aged Severity of Illness Index ROC Curve Reproducibility of Results Aged Adult Sensitivity and Specificity Neural Networks, Computer Retrospective Studies Female Humans
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Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy.

Nature biomedical engineering Oct 10, 2018
The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine-learni...
Colonic Neoplasms Colonoscopy Colonic Polyps Humans Deep Learning Software Image Interpretation, Computer-Assisted ROC Curve Algorithms Precancerous Conditions Area Under Curve Databases, Factual
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Explainable machine-learning predictions for the prevention of hypoxaemia during surgery.

Nature biomedical engineering Oct 10, 2018
Although anaesthesiologists strive to avoid hypoxemia during surgery, reliably predicting future intraoperative hypoxemia is not currently possible. Here, we report the development and testing of a machine-learning-based system that, in real time dur...
Male Middle Aged ROC Curve Area Under Curve Risk Factors Anesthesia, General Humans Aged Hypoxia Machine Learning Aged, 80 and over Surgical Procedures, Operative Electronic Health Records Adult Anesthesiologists Female
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Accuracy of an artificial neural network for detecting a regional abnormality in myocardial perfusion SPECT.

Annals of nuclear medicine Oct 9, 2018
OBJECTIVES: The patient-based diagnosis with an artificial neural network (ANN) has shown potential utility for the detection of coronary artery disease; however, the region-based accuracy of the detected regions has not been fully evaluated. The aim...
Aged Male Middle Aged ROC Curve Humans Neural Networks, Computer Female Technetium Tc 99m Sestamibi Image Processing, Computer-Assisted Area Under Curve Tomography, Emission-Computed, Single-Photon Myocardial Perfusion Imaging
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Comparison of 2 Natural Language Processing Methods for Identification of Bleeding Among Critically Ill Patients.

JAMA network open Oct 5, 2018
IMPORTANCE: To improve patient safety, health care systems need reliable methods to detect adverse events in large patient populations. Events are often described in clinical notes, rather than structured data, which make them difficult to identify o...
Hemorrhage Middle Aged ROC Curve Support Vector Machine Aged Humans Natural Language Processing Aged, 80 and over Machine Learning Critical Illness Sensitivity and Specificity Electronic Health Records Male Female
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Towards a modular decision support system for radiomics: A case study on rectal cancer.

Artificial intelligence in medicine Oct 4, 2018
Following the personalized medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncolo...
Humans Machine Learning Precision Medicine ROC Curve Image Processing, Computer-Assisted Rectal Neoplasms Decision Making, Computer-Assisted
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Detecting drug-resistant tuberculosis in chest radiographs.

International journal of computer assisted radiology and surgery Oct 3, 2018
PURPOSE: Tuberculosis is a major global health threat claiming millions of lives each year. While the total number of tuberculosis cases has been decreasing over the last years, the rise of drug-resistant tuberculosis has reduced the chance of contro...
Male Middle Aged Pilot Projects ROC Curve Probability Radiography, Thoracic Diagnosis, Differential Neural Networks, Computer Humans Machine Learning Tuberculosis, Multidrug-Resistant Tomography, X-Ray Computed Female
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Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.

European radiology Oct 2, 2018
OBJECTIVE: We aimed to identify optimal machine-learning methods for preoperative differentiation of sacral chordoma (SC) and sacral giant cell tumour (SGCT) based on 3D non-enhanced computed tomography (CT) and CT-enhanced (CTE) features.
Adult Male Middle Aged Machine Learning Linear Models Imaging, Three-Dimensional Giant Cell Tumors ROC Curve Humans Retrospective Studies Spinal Neoplasms Support Vector Machine Sacrum Tomography, X-Ray Computed Diagnosis, Differential Chordoma Female Image Interpretation, Computer-Assisted
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