AIMC Topic: Age Factors

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Deep learning-based survival prediction for multiple cancer types using histopathology images.

PloS one Jun 17, 2020
Providing prognostic information at the time of cancer diagnosis has important implications for treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic ...
Sex Factors Humans Survival Analysis Prognosis Datasets as Topic Age Factors Risk Factors Neoplasm Staging Adult Feasibility Studies Male Middle Aged Risk Assessment Neoplasms Deep Learning Image Processing, Computer-Assisted Female
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AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction.

NeuroImage. Clinical Jun 1, 2020
The prediction of Mild Cognitive Impairment (MCI) patients who are at higher risk converting to Alzheimer's Disease (AD) is critical for effective intervention and patient selection in clinical trials. Different biomarkers including neuroimaging have...
Pattern Recognition, Automated Neuroimaging Cognitive Dysfunction Humans Age Factors Neural Networks, Computer Predictive Value of Tests Brain Alzheimer Disease Disease Progression Image Interpretation, Computer-Assisted
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Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning.

Epilepsia May 20, 2020
OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics.
Psychology Middle Aged Humans Unsupervised Machine Learning Quality of Life Female Epilepsy Socioeconomic Factors Adult Age Factors Sex Factors Cross-Sectional Studies Cluster Analysis Age of Onset Male Patient Reported Outcome Measures
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Artificial intelligence may offer insight into factors determining individual TSH level.

PloS one May 20, 2020
The factors that determine Serum Thyrotropin (TSH) levels have been examined through different methods, using different covariates. However, the use of machine learning methods has so far not been studied in population databases like NHANES (National...
Age Factors Thyrotropin Thyroxine Ethnicity Triiodothyronine Machine Learning Humans Retrospective Studies Artificial Intelligence Female Male Body Mass Index
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Unsupervised Cluster Analysis of Patients With Aortic Stenosis Reveals Distinct Population With Different Phenotypes and Outcomes.

Circulation. Cardiovascular imaging May 18, 2020
BACKGROUND: There is a lack of studies investigating the heterogeneity of patients with aortic stenosis (AS). We explored whether cluster analysis identifies distinct subgroups with different prognostic significances in AS.
Predictive Value of Tests Cause of Death Female Age Factors Reproducibility of Results Humans Cluster Analysis Risk Factors Decision Support Techniques Aortic Valve Stenosis Aged Phenotype Comorbidity Male Aortic Valve Aged, 80 and over Prospective Studies Severity of Illness Index Unsupervised Machine Learning Echocardiography Prognosis Hemodynamics Middle Aged
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An automated machine learning approach to predict brain age from cortical anatomical measures.

Human brain mapping May 16, 2020
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a m...
Young Adult Adult Male Middle Aged Adolescent Humans Machine Learning Female Aged Aged, 80 and over Datasets as Topic Neuroimaging Cerebral Cortex Image Processing, Computer-Assisted Models, Theoretical Age Factors Magnetic Resonance Imaging Image Interpretation, Computer-Assisted
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Digital conversations about suicide among teenagers and adults with epilepsy: A big-data, machine learning analysis.

Epilepsia May 8, 2020
OBJECTIVE: Digital media conversations can provide important insight into the concerns and struggles of people with epilepsy (PWE) outside of formal clinical settings and help generate useful information for treatment planning. Our study aimed to exp...
Humans Machine Learning Female Adolescent Young Adult Suicide Adult Social Media Age Factors Male Natural Language Processing Social Support Epilepsy Big Data
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Development and Validation of Machine Learning Algorithms for Predicting Adverse Events After Surgery for Lumbar Degenerative Spondylolisthesis.

World neurosurgery Apr 25, 2020
BACKGROUND: Preoperative prognostication of adverse events (AEs) for patients undergoing surgery for lumbar degenerative spondylolisthesis (LDS) can improve risk stratification and help guide the surgical decision-making process. The aim of this stud...
Spondylolisthesis Decision Making Prognosis Clinical Decision Rules Functional Status Female Neurosurgical Procedures Risk Factors Spinal Fusion Lumbar Vertebrae Middle Aged Humans Aged Alkaline Phosphatase Bone Transplantation Postoperative Complications Machine Learning Aged, 80 and over Ilium Sex Factors Serum Albumin Adolescent Algorithms Young Adult Transplantation, Autologous Logistic Models Adult Age Factors Male
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Birds have peramorphic skulls, too: anatomical network analyses reveal oppositional heterochronies in avian skull evolution.

Communications biology Apr 24, 2020
In contrast to the vast majority of reptiles, the skulls of adult crown birds are characterized by a high degree of integration due to bone fusion, e.g., an ontogenetic event generating a net reduction in the number of bones. To understand this proce...
Neural Networks, Computer Animals Birds Phylogeny Age Factors Skull Biological Evolution Dinosaurs
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From a deep learning model back to the brain-Identifying regional predictors and their relation to aging.

Human brain mapping Apr 22, 2020
We present a Deep Learning framework for the prediction of chronological age from structural magnetic resonance imaging scans. Previous findings associate increased brain age with neurodegenerative diseases and higher mortality rates. However, the im...
Aging Middle Aged Deep Learning Adolescent Child, Preschool Child Age Factors Magnetic Resonance Imaging Datasets as Topic Neuroimaging Aged Aged, 80 and over Humans Young Adult Brain Adult Female Male
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