AIMC Topic: Severity of Illness Index

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Machine learning-based prediction of radiographic progression in patients with axial spondyloarthritis.

Clinical rheumatology Oct 30, 2019
INTRODUCTION: Machine learning is applied to characterize the risk and predict outcomes in multi-dimensional data. The prediction of radiographic progression in axial spondyloarthritis (axSpA) remains limited. Hence, we tested the feasibility of supe...
Humans Machine Learning Retrospective Studies Republic of Korea Female Radiography Spine Spondylarthritis Adult Male Middle Aged Disease Progression Severity of Illness Index
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The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology Oct 28, 2019
Machine learning (ML) applied to patient-reported (PROs) and clinical-assessed outcomes (CAOs) could favour a more predictive and personalized medicine. Our aim was to confirm the important role of applying ML to PROs and CAOs of people with relapsin...
Humans Machine Learning Proof of Concept Study Disease Progression Severity of Illness Index Outcome Assessment, Health Care Multiple Sclerosis Prognosis Patient Reported Outcome Measures Female Adult Male Middle Aged
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Impact of age at onset on the phenomenology of depression in treatment-seeking adults in the STAR*D trial.

Journal of affective disorders Oct 26, 2019
BACKGROUND: - Adolescence is characterized by biological, emotional, and behavioral changes. The onset of depression during this vulnerable time may confer specific risks. This study examined whether symptoms of depression were associated with age at...
Regression Analysis Randomized Controlled Trials as Topic Age of Onset Retrospective Studies Patient Acceptance of Health Care Female Humans Young Adult Neural Networks, Computer Middle Aged Adult Prospective Studies Severity of Illness Index Male Depression Adolescent Child
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Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder.

Drug and alcohol dependence Oct 22, 2019
BACKGROUND: Substance use disorder (SUD) exacts enormous societal costs in the United States, and it is important to detect high-risk youths for prevention. Machine learning (ML) is the method to find patterns and make prediction from data. We hypoth...
Humans Machine Learning Female Reproducibility of Results Young Adult Adult Male Severity of Illness Index Adolescent Outcome Assessment, Health Care Child ROC Curve Psychological Techniques Longitudinal Studies Predictive Value of Tests Substance-Related Disorders
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Identification of a blood-based 12-gene signature that predicts the severity of coronary artery stenosis: An integrative approach based on gene network construction, Support Vector Machine algorithm, and multi-cohort validation.

Atherosclerosis Oct 9, 2019
BACKGROUND AND AIMS: We aimed to identify a blood-based gene expression score (GES) to predict the severity of coronary artery stenosis in patients with known or suspected coronary artery disease (CAD) by integrative use of gene network construction,...
Genetic Predisposition to Disease Nomograms Support Vector Machine Predictive Value of Tests Coronary Stenosis Reproducibility of Results Risk Factors Aged Male Humans Middle Aged Decision Support Techniques Severity of Illness Index Phenotype Gene Expression Profiling Retrospective Studies Transcriptome Female China Gene Regulatory Networks
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CNN-based diagnosis models for canine ulcerative keratitis.

Scientific reports Oct 2, 2019
The purpose of this methodological study was to develop a convolutional neural network (CNN), which is a recently developed deep-learning-based image recognition method, to determine corneal ulcer severity in dogs. The CNN model was trained with imag...
Software Retrospective Studies Female Cornea Animals Male Dog Diseases Severity of Illness Index Models, Biological Deep Learning Datasets as Topic Image Processing, Computer-Assisted Pattern Recognition, Automated Dogs Corneal Ulcer
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Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity.

Drug and alcohol dependence Oct 1, 2019
BACKGROUND: This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics.
Humans Child Machine Learning Female Longitudinal Studies Reproducibility of Results Predictive Value of Tests Young Adult Substance-Related Disorders Adult Psychological Techniques Male Severity of Illness Index Adolescent
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Detection of Hemodynamically Significant Coronary Stenosis: CT Myocardial Perfusion versus Machine Learning CT Fractional Flow Reserve.

Radiology Sep 24, 2019
Background Direct intraindividual comparison of dynamic CT myocardial perfusion imaging (MPI) and machine learning (ML)-based CT fractional flow reserve (FFR) has not been explored for diagnosing hemodynamically significant coronary artery disease. P...
Adult Sensitivity and Specificity Humans Coronary Stenosis Prospective Studies Hemodynamics Machine Learning Fractional Flow Reserve, Myocardial Male Tomography, X-Ray Computed ROC Curve Middle Aged Female Coronary Angiography Severity of Illness Index Aged Radiographic Image Interpretation, Computer-Assisted Aged, 80 and over Computed Tomography Angiography
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Development and accuracy of an artificial intelligence algorithm for acne grading from smartphone photographs.

Experimental dermatology Sep 9, 2019
We developed an artificial intelligence algorithm (AIA) for smartphones to determine the severity of facial acne using the GEA scale and to identify different types of acne lesion (comedonal, inflammatory) and postinflammatory hyperpigmentation (PIHP...
Acne Vulgaris Severity of Illness Index Adolescent Child Smartphone Mobile Applications Young Adult Humans Adult Artificial Intelligence Male Female Middle Aged
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Machine Learning to Understand the Immune-Inflammatory Pathways in Fibromyalgia.

International journal of molecular sciences Aug 29, 2019
Fibromyalgia (FM) is a chronic syndrome characterized by widespread musculoskeletal pain, and physical and emotional symptoms. Although its pathophysiology is largely unknown, immune-inflammatory pathways may be involved. We examined serum interleuki...
Depression Middle Aged Humans Inflammation Mediators Surveys and Questionnaires Machine Learning Quality of Life Multivariate Analysis Signal Transduction Anxiety Fibromyalgia Cytokines C-Reactive Protein Severity of Illness Index Female Pain Psychiatric Status Rating Scales
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