AIMC Topic: Predictive Value of Tests
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Artificial intelligence and cancer diagnosis: caution needed.
The Lancet. Oncology
Sep 9, 2021
Discrimination of vascular aging using the arterial pulse spectrum and machine-learning analysis.
Microvascular research
Sep 8, 2021
Aging contributes to the progression of vascular dysfunction and is a major nonreversible risk factor for cardiovascular disease. The aim of this study was to determine the effectiveness of using arterial pulse-wave measurements, frequency-domain pul...
Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.
European journal of cancer (Oxford, England : 1990)
Sep 8, 2021
BACKGROUND: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinic...
Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women.
Radiology
Sep 7, 2021
Background The ability of deep learning (DL) models to classify women as at risk for either screening mammography-detected or interval cancer (not detected at mammography) has not yet been explored in the literature. Purpose To examine the ability of...
Revisiting performance metrics for prediction with rare outcomes.
Statistical methods in medical research
Sep 1, 2021
Machine learning algorithms are increasingly used in the clinical literature, claiming advantages over logistic regression. However, they are generally designed to maximize the area under the receiver operating characteristic curve. While area under ...
A machine-learning-based method to predict adverse events in patients with dilated cardiomyopathy and severely reduced ejection fractions.
The British journal of radiology
Aug 31, 2021
OBJECTIVE: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of experiencing adverse cardiac events. A machine learning (ML) method could enable more effective risk stra...
Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction.
IEEE transactions on neural networks and learning systems
Aug 31, 2021
Electronic health records (EHRs) are characterized as nonstationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inherent within them. In particular, sparseness caused mostly by many missi...
Exploring the diagnostic effectiveness for myocardial ischaemia based on CCTA myocardial texture features.
BMC cardiovascular disorders
Aug 31, 2021
BACKGROUND: To explore the characteristics of myocardial textures on coronary computed tomography angiography (CCTA) images in patients with coronary atherosclerotic heart disease, a classification model was established, and the diagnostic effectiven...
Predictive Value of Tests
Coronary Angiography
Radiographic Image Interpretation, Computer-Assisted
Reproducibility of Results
Coronary Artery Disease
Aged
Multidetector Computed Tomography
Male
Humans
Computed Tomography Angiography
Middle Aged
Proof of Concept Study
Machine Learning
Myocardial Ischemia
Retrospective Studies
Female
The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma.
Cancer medicine
Aug 28, 2021
This retrospective study has been conducted to validate the performance of deep learning-based survival models in glioblastoma (GBM) patients alongside the Cox proportional hazards model (CoxPH) and the random survival forest (RSF). Furthermore, the ...
Bayes Theorem
Sex Factors
Survival Analysis
DNA Modification Methylases
Proportional Hazards Models
Chemoradiotherapy
Humans
Radiotherapy
Retrospective Studies
DNA Repair Enzymes
Isocitrate Dehydrogenase
Female
Tumor Suppressor Proteins
Deep Learning
Karnofsky Performance Status
Aged
Adult
Age Factors
Brain Neoplasms
Predictive Value of Tests
Male
Glioblastoma
Middle Aged
Machine Learning-Based Individualized Survival Prediction Model for Total Knee Replacement in Osteoarthritis: Data From the Osteoarthritis Initiative.
Arthritis care & research
Aug 26, 2021
OBJECTIVE: By using machine learning, our study aimed to build a model to predict risk and time to total knee replacement (TKR) of an osteoarthritic knee.
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