AIMC Topic:
Predictive Value of Tests

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Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review.

Progress in cardiovascular diseases Mar 19, 2020
There has been a tidal wave of recent interest in artificial intelligence (AI), machine learning and deep learning approaches in cardiovascular (CV) medicine. In the era of modern medicine, AI and electronic health records hold the promise to improve...
Diagnosis, Computer-Assisted Deep Learning Support Vector Machine Multimodal Imaging Predictive Value of Tests Image Interpretation, Computer-Assisted Humans Machine Learning Reproducibility of Results Cardiovascular Diseases
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Deep learning approaches for sleep disorder prediction in an asthma cohort.

The Journal of asthma : official journal of the Association for the Care of Asthma Mar 18, 2020
OBJECTIVE: Sleep is a natural activity of humans that affects physical and mental health; therefore, sleep disturbance may lead to fatigue and lower productivity. This study examined 1 million samples included in the Taiwan National Health Insurance ...
Deep Learning Adolescent Child, Preschool Child Nerve Tissue Proteins Predictive Value of Tests DNA-Binding Proteins Sleep Wake Disorders Young Adult Adult Asthma Male Middle Aged Infant Humans Artificial Intelligence Female
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Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine.

JACC. Cardiovascular imaging Mar 18, 2020
OBJECTIVES: The authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac event(s) (MACE)...
Middle Aged Patient Readmission Disease Progression Aged Humans Ventricular Dysfunction, Left Unsupervised Machine Learning Aged, 80 and over Prospective Studies Image Interpretation, Computer-Assisted Predictive Value of Tests Adult Prognosis Pattern Recognition, Automated Registries Male Retrospective Studies Ventricular Function, Left Female Echocardiography
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Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy.

Circulation. Arrhythmia and electrophysiology Mar 18, 2020
BACKGROUND: Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-...
Male Ventricular Function, Left Stroke Volume Gadolinium DTPA Middle Aged Death, Sudden, Cardiac Risk Assessment Diagnosis, Computer-Assisted Prognosis Humans Retrospective Studies Registries Machine Learning Imaging, Three-Dimensional Female Arrhythmias, Cardiac Risk Factors United States Heart Rate Aged Contrast Media Myocardial Ischemia Magnetic Resonance Imaging Cardiomyopathies Action Potentials Fourier Analysis Predictive Value of Tests
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Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.

European radiology Mar 11, 2020
BACKGROUND AND PURPOSE: Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features cou...
Humans Machine Learning Brain Prognosis Mutation Survival Analysis Isocitrate Dehydrogenase Male Middle Aged ROC Curve Retrospective Studies Glioma Female Magnetic Resonance Imaging Reproducibility of Results Predictive Value of Tests Adult Brain Neoplasms
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Prediction of prognosis in patients with tetralogy of Fallot based on deep learning imaging analysis.

Heart (British Cardiac Society) Mar 11, 2020
OBJECTIVE: To assess the utility of machine learning algorithms for automatically estimating prognosis in patients with repaired tetralogy of Fallot (ToF) using cardiac magnetic resonance (CMR).
Germany Tetralogy of Fallot Magnetic Resonance Imaging, Cine Humans Prospective Studies Prognosis Child Risk Assessment Female Electrocardiography Diagnosis, Computer-Assisted Risk Factors Predictive Value of Tests Deep Learning Young Adult Registries Adolescent Adult Feasibility Studies Time Factors Male Image Interpretation, Computer-Assisted Middle Aged
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Machine Learning in Drug Discovery and Development Part 1: A Primer.

CPT: pharmacometrics & systems pharmacology Mar 11, 2020
Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and develop...
Drug Discovery Models, Theoretical Drug Development Predictive Value of Tests Drug Approval Humans History, 20th Century Artificial Intelligence Machine Learning Algorithms
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Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.

Ophthalmology Mar 10, 2020
PURPOSE: To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss.
Longitudinal Studies Monitoring, Physiologic Visual Acuity Vision Disorders Predictive Value of Tests Visual Fields Adult Sensitivity and Specificity Cross-Sectional Studies Humans Male Glaucoma Middle Aged Artificial Intelligence Retrospective Studies Female Aged False Negative Reactions Optic Nerve Diseases
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Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.

Communications biology Mar 9, 2020
Efficient action control is indispensable for goal-directed behaviour. Different theories have stressed the importance of either attention or response selection sub-processes for action control. Yet, it is unclear to what extent these processes can b...
Attention Goals Brain Waves Neuropsychological Tests Motor Activity Conflict, Psychological Support Vector Machine Predictive Value of Tests Electroencephalography Signal Processing, Computer-Assisted Reaction Time Humans Brain Female Young Adult Adult Male Deep Learning Adolescent Cognition
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Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.

Clinical cancer research : an official journal of the American Association for Cancer Research Mar 5, 2020
PURPOSE: Between 30%-40% of patients with prostate cancer experience disease recurrence following radical prostatectomy. Existing clinical models for recurrence risk prediction do not account for population-based variation in the tumor phenotype, des...
Middle Aged Risk Assessment Disease Progression ROC Curve Image Processing, Computer-Assisted Nomograms Humans Machine Learning Prognosis Survival Rate Biomarkers, Tumor Male Black or African American Prostatectomy Predictive Value of Tests Prostatic Neoplasms Prostate-Specific Antigen Neoplasm Recurrence, Local Stromal Cells
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