AIMC Topic:
Predictive Value of Tests

Clear Filters Showing 1271 to 1280 of 2129 articles

CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.

International journal of computer assisted radiology and surgery Nov 13, 2019
PURPOSE: As some of the most important factors for treatment decision of lung cancer (which is the deadliest neoplasm) are staging and histology, this work aimed to associate quantitative contrast-enhanced computed tomography (CT) features from malig...
Middle Aged Lung ROC Curve Lung Neoplasms Predictive Value of Tests Neoplasm Staging Biopsy Neoplasm Metastasis Female Aged Aged, 80 and over Adult Male Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed
View on PubMed DOI

Machine Learning for Pretest Probability of Obstructive Coronary Stenosis in Symptomatic Patients.

JACC. Cardiovascular imaging Nov 13, 2019
Computed Tomography Angiography Coronary Stenosis Severity of Illness Index Diagnosis, Computer-Assisted Predictive Value of Tests Coronary Angiography Humans Coronary Artery Disease Machine Learning Female Reproducibility of Results Male Middle Aged
View on PubMed DOI

Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography.

IEEE transactions on medical imaging Nov 12, 2019
In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed duri...
Humans Retrospective Studies Deep Learning Predictive Value of Tests Fractional Flow Reserve, Myocardial Coronary Angiography Coronary Artery Disease Coronary Vessels Computed Tomography Angiography
View on PubMed DOI

Predicting rehospitalization within 2 years of initial patient admission for a major depressive episode: a multimodal machine learning approach.

Translational psychiatry Nov 11, 2019
Machine learning methods show promise to translate univariate biomarker findings into clinically useful multivariate decision support systems. At current, works in major depressive disorder have predominantly focused on neuroimaging and clinical pred...
Follow-Up Studies Magnetic Resonance Imaging Adult Predictive Value of Tests Male Area Under Curve Humans Biomarkers Antidepressive Agents Middle Aged Patient Readmission Machine Learning Brain Depressive Disorder, Major Female Treatment Outcome Aged
View on PubMed DOI

Artificial plant optimization algorithm to detect heart rate & presence of heart disease using machine learning.

Artificial intelligence in medicine Nov 8, 2019
In today's world, cardiovascular diseases are prevalent becoming the leading cause of death; more than half of the cardiovascular diseases are due to Coronary Heart Disease (CHD) which generates the demand of predicting them timely so that people can...
Heart Diseases Databases, Factual Humans Heart Rate Support Vector Machine Artificial Intelligence Coronary Disease Predictive Value of Tests Machine Learning Plethysmography Algorithms Plants Photosynthesis Programming Languages
View on PubMed DOI

Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma.

BMC medical informatics and decision making Nov 8, 2019
BACKGROUND: Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. Recently, deep learning models have achieved state-of-the-a...
Child Logistic Models Predictive Value of Tests Clinical Decision-Making Asthma Risk Assessment Deep Learning Child, Preschool Humans Algorithms Prognosis Electronic Health Records
View on PubMed DOI

ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia.

BMC medical informatics and decision making Nov 7, 2019
BACKGROUND: The hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT). Accurate discrimination between IDA and TT is an important issue and better methods are urgently needed. Altho...
Humans Neural Networks, Computer Male Middle Aged Thailand Adolescent ROC Curve Machine Learning Retrospective Studies Female Young Adult Adult Internet Cluster Analysis Decision Trees Diagnosis, Differential Predictive Value of Tests Anemia, Iron-Deficiency beta-Thalassemia Anemia, Hypochromic
View on PubMed DOI

Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.

European journal of radiology Nov 6, 2019
PURPOSE: To evaluate the performance of machine learning (ML)-based computed tomography (CT) radiomics analysis for discriminating between low grade (WHO/ISUP I-II) and high grade (WHO/ISUP III-IV) clear cell renal cell carcinomas (ccRCCs).
Young Adult Adult Male Middle Aged Image Interpretation, Computer-Assisted Logistic Models Kidney Humans Kidney Neoplasms Support Vector Machine Machine Learning Carcinoma, Renal Cell Female Diagnosis, Differential Algorithms Reproducibility of Results Predictive Value of Tests Retrospective Studies Aged Neoplasm Grading Tomography, X-Ray Computed Aged, 80 and over
View on PubMed DOI

Computer-assisted assessment of colonic polyp histopathology using probe-based confocal laser endomicroscopy.

International journal of colorectal disease Nov 6, 2019
INTRODUCTION: Probe-based confocal laser endomicroscopy (pCLE) is a promising modality for classifying polyp histology in vivo, but decision making in real-time is hampered by high-magnification targeting and by the learning curve for image interpret...
Machine Learning Colonic Polyps Microscopy, Confocal Humans Image Interpretation, Computer-Assisted Male Predictive Value of Tests Middle Aged Feasibility Studies Colonoscopy Colonic Neoplasms Diagnosis, Computer-Assisted Tumor Burden Observer Variation Clinical Competence Female Reproducibility of Results Aged Aged, 80 and over
View on PubMed DOI

A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.

BMC medical informatics and decision making Nov 6, 2019
BACKGROUND: Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of mach...
Humans Machine Learning Logistic Models Nutrition Surveys Diabetes Mellitus Electronic Health Records Support Vector Machine Predictive Value of Tests Female Male Cardiovascular Diseases ROC Curve
View on PubMed DOI
Popular Topics
Recent Journals