AIMC Topic: Case-Control Studies

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Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system.

Aging Mar 3, 2021
We developed and validated a new prognostic model for predicting the overall survival in clear cell renal cell carcinoma (ccRCC) patients. In this study, artificial intelligence (AI) algorithms including random forest and neural network were trained ...
Algorithms Kaplan-Meier Estimate Prognosis Carcinoma, Renal Cell Female Genes, Neoplasm Male Middle Aged Survival Analysis Humans Kidney Neoplasms Artificial Intelligence Proportional Hazards Models Case-Control Studies Nomograms Models, Statistical
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Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case-control study.

American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics Mar 1, 2021
This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learning (ML). We built on the methodology of prior studies to obtain more generalizable/reproducible resul...
Biomarkers Brain Female Aged Aged, 80 and over Humans Machine Learning Case-Control Studies Young Adult ROC Curve Adolescent Adult Computational Biology Cohort Studies Male RNA, Messenger Middle Aged Depressive Disorder, Major
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Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.

Interdisciplinary sciences, computational life sciences Feb 27, 2021
Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic ...
Humans COVID-19 Reproducibility of Results Lung Radiographic Image Interpretation, Computer-Assisted Case-Control Studies Multidetector Computed Tomography Diagnosis, Computer-Assisted Pneumonia, Bacterial Deep Learning Pneumonia, Viral Diagnosis, Differential Predictive Value of Tests
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Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images.

Annals of the New York Academy of Sciences Feb 26, 2021
We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mice. OCT angiograms were acquired with a custom-built OCT system based on an akinetic swept laser at ...
Diabetes Mellitus, Experimental Algorithms Angiography Female Ear Reproducibility of Results Machine Learning Mice Deep Learning Animals Diagnosis, Differential ROC Curve Case-Control Studies Tomography, Optical Coherence Image Processing, Computer-Assisted
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Convolutional Neural Network-Based Deep Learning Model for Predicting Differential Suicidality in Depressive Patients Using Brain Generalized q-Sampling Imaging.

The Journal of clinical psychiatry Feb 23, 2021
OBJECTIVE: Suicide is a priority health problem. Suicide assessment depends on imperfect clinician assessment with minimal ability to predict the risk of suicide. Machine learning/deep learning provides an opportunity to detect an individual at risk ...
Adult Male Middle Aged Risk Assessment Suicide, Attempted Humans Depressive Disorder Neural Networks, Computer Case-Control Studies Interview, Psychological Algorithms Deep Learning Brain Suicidal Ideation Female Magnetic Resonance Imaging Young Adult Neuroimaging
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Efficacy of Smart Speaker-Based Metamemory Training in Older Adults: Case-Control Cohort Study.

Journal of medical Internet research Feb 16, 2021
BACKGROUND: Metamemory training (MMT) is a useful training strategy for improving cognitive functioning in the older adult population. Despite the advantages, there are limitations imposed by location and time constraints.
Teaching Metacognition Female Male Middle Aged Case-Control Studies Cohort Studies Humans Artificial Intelligence
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Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer's disease: Machine learning approaches.

Journal of psychopharmacology (Oxford, England) Feb 15, 2021
BACKGROUND: d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing.
Female Aged Aged, 80 and over Sensitivity and Specificity Male Middle Aged Humans Machine Learning Algorithms Biomarkers Chromatography, High Pressure Liquid Alzheimer Disease Glutamic Acid Severity of Illness Index Case-Control Studies Psychiatric Status Rating Scales Cognitive Dysfunction Diagnosis, Differential
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Relation of Quantitative Histologic and Radiologic Breast Tissue Composition Metrics With Invasive Breast Cancer Risk.

JNCI cancer spectrum Feb 6, 2021
BACKGROUND: Benign breast disease (BBD) is a strong breast cancer risk factor, but identifying patients that might develop invasive breast cancer remains a challenge.
Breast Supervised Machine Learning Confidence Intervals Odds Ratio Breast Density Breast Diseases Case-Control Studies Diagnosis, Computer-Assisted Age Factors Mammography Biopsy Aged, 80 and over Young Adult Adult Breast Neoplasms Humans Middle Aged Female Risk Factors Aged
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Validation of a Machine Learning Brain Electrical Activity-Based Index to Aid in Diagnosing Concussion Among Athletes.

JAMA network open Feb 1, 2021
IMPORTANCE: An objective, reliable indicator of the presence and severity of concussive brain injury and of the readiness for the return to activity has the potential to reduce concussion-related disability.
Athletes Brain Concussion Athletic Injuries Mental Status and Dementia Tests Glasgow Coma Scale Case-Control Studies Adolescent Universities Electroencephalography Schools Return to Sport Humans Machine Learning Prospective Studies Brain Female Reproducibility of Results Young Adult Male
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Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease.

BMC cardiovascular disorders Jan 28, 2021
BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learnin...
Genetic Association Studies Gene Expression Profiling Atrial Fibrillation Humans Proteomics Transcriptome Machine Learning Heart Atria Signal Transduction Case-Control Studies Female Heart Valve Diseases Protein Interaction Maps Databases, Genetic Male Oligonucleotide Array Sequence Analysis Gene Regulatory Networks Bayes Theorem Middle Aged
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