AIMC Topic:
ROC Curve
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Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.
Journal of translational medicine
Dec 7, 2020
BACKGROUND: Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible predicti...
Predicting cryptic ligand binding sites based on normal modes guided conformational sampling.
Proteins
Dec 5, 2020
To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by norm...
Karyopherins
Protein Tyrosine Phosphatase, Non-Receptor Type 1
Exportin 1 Protein
Receptors, Cytoplasmic and Nuclear
Binding Sites
Coronavirus 3C Proteases
Coronavirus Papain-Like Proteases
Regression Analysis
Area Under Curve
Antigens, CD
Molecular Dynamics Simulation
Hepacivirus
Ligands
Elasticity
Protein Conformation
Humans
Computational Biology
Machine Learning
Antigens, Neoplasm
Interleukin-2
Models, Statistical
Algorithms
Reproducibility of Results
SARS-CoV-2
ROC Curve
Multi-path synergic fusion deep neural network framework for breast mass classification using digital breast tomosynthesis.
Physics in medicine and biology
Dec 4, 2020
OBJECTIVE: To develop and evaluate a multi-path synergic fusion (MSF) deep neural network model for breast mass classification using digital breast tomosynthesis (DBT).
StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images.
Physical and engineering sciences in medicine
Dec 4, 2020
The highly contagious nature of Coronavirus disease 2019 (Covid-19) resulted in a global pandemic. Due to the relatively slow and taxing nature of conventional testing for Covid-19, a faster method needs to be in place. The current researches have su...
Simultaneous Diagnosis of Severity and Features of Diabetic Retinopathy in Fundus Photography Using Deep Learning.
IEEE journal of biomedical and health informatics
Dec 4, 2020
Deep learning methods for diabetic retinopathy (DR) diagnosis are usually criticized as being lack of interpretability in the diagnostic result, thus limiting their application in clinic. Simultaneous prediction of DR related features during the DR s...
Hard Attention Net for Automatic Retinal Vessel Segmentation.
IEEE journal of biomedical and health informatics
Dec 4, 2020
Automated retinal vessel segmentation is among the most significant application and research topics in ophthalmologic image analysis. Deep learning based retinal vessel segmentation models have attracted much attention in the recent years. However, c...
Ensemble machine learning for the prediction of patient-level outcomes following thyroidectomy.
American journal of surgery
Dec 3, 2020
BACKGROUND: Accurate prediction of thyroidectomy complications is necessary to inform treatment decisions. Ensemble machine learning provides one approach to improve prediction.
Estimating visual field loss from monoscopic optic disc photography using deep learning model.
Scientific reports
Dec 3, 2020
Visual field assessment is recognized as the important criterion of glaucomatous damage judgement; however, it can show large test-retest variability. We developed a deep learning (DL) algorithm that quantitatively predicts mean deviation (MD) of sta...
Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study.
Journal of medical Internet research
Dec 2, 2020
BACKGROUND: Conventional diagnosis of COVID-19 with reverse transcription polymerase chain reaction (RT-PCR) testing (hereafter, PCR) is associated with prolonged time to diagnosis and significant costs to run the test. The SARS-CoV-2 virus might lea...
Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram.
International journal of cardiology
Dec 1, 2020
INTRODUCTION: Early detection and intervention of atrial fibrillation (AF) is a cornerstone for effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have been developed, but they could not be applied in clinical practi...
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