AIMC Topic: Longitudinal Studies

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Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study.

Cells Apr 30, 2022
Blood biomarkers for dementia have the potential to identify preclinical disease and improve participant selection for clinical trials. Machine learning is an efficient analytical strategy to simultaneously identify multiple candidate biomarkers for ...
Machine Learning Biomarkers Female Male Humans Neural Networks, Computer Dementia Longitudinal Studies
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Assessment of the predictive potential of cognitive scores from retinal images and retinal fundus metadata via deep learning using the CLSA database.

Scientific reports Apr 6, 2022
Accumulation of beta-amyloid in the brain and cognitive decline are considered hallmarks of Alzheimer's disease. Knowing from previous studies that these two factors can manifest in the retina, the aim was to investigate whether a deep learning metho...
Cognition Longitudinal Studies Fundus Oculi Metadata Deep Learning Canada
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Machine learning-aided risk prediction for metabolic syndrome based on 3 years study.

Scientific reports Feb 10, 2022
Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding...
Humans Machine Learning Female Models, Statistical Middle Aged Risk Factors Physical Examination Risk Assessment Adult Longitudinal Studies Male Metabolic Syndrome
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An Interpretable Machine Learning Approach to Predict Fall Risk Among Community-Dwelling Older Adults: a Three-Year Longitudinal Study.

Journal of general internal medicine Feb 2, 2022
BACKGROUND: Adverse health effects resulting from falls are a major public health concern. Although studies have identified risk factors for falls, none have examined long-term prediction of fall risk. Furthermore, recent evidence suggests that there...
Independent Living Humans Machine Learning Prospective Studies Risk Factors Aged Longitudinal Studies
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Interpretable machine learning for high-dimensional trajectories of aging health.

PLoS computational biology Jan 10, 2022
We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine ...
Humans Machine Learning Aging Health Transition Computational Biology Longitudinal Studies Health Status Models, Biological
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A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research Dec 22, 2021
OBJECTIVES: We propose a framework of health outcomes modeling with dynamic decision making and real-world data (RWD) to evaluate the potential utility of novel risk prediction models in clinical practice. Lung transplant (LTx) referral decisions in ...
Risk Assessment Humans Longitudinal Studies Machine Learning Forced Expiratory Volume Research Design Lung Transplantation Tissue and Organ Procurement Outcome Assessment, Health Care Cystic Fibrosis Data Collection Referral and Consultation Survival Analysis
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A deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure.

Nature communications Dec 3, 2021
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical...
Neuroimaging Cluster Analysis Alzheimer Disease Healthy Volunteers Middle Aged Case-Control Studies Deep Learning Cognitive Dysfunction Image Processing, Computer-Assisted Longitudinal Studies Magnetic Resonance Imaging Female Aged Aged, 80 and over Humans Male Brain
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Network analysis of trauma in patients with early-stage psychosis.

Scientific reports Nov 23, 2021
Childhood trauma (ChT) is a risk factor for psychosis. Negative lifestyle factors such as rumination, negative schemas, and poor diet and exercise are common in psychosis. The present study aimed to perform a network analysis of interactions between ...
Adult Cross-Sectional Studies Male Middle Aged Psychiatric Status Rating Scales Surveys and Questionnaires Longitudinal Studies Neural Networks, Computer Female Risk Factors Young Adult Humans Self Report Psychotic Disorders Wounds and Injuries
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Development and Validation of a Deep Learning Model for Earlier Detection of Cognitive Decline From Clinical Notes in Electronic Health Records.

JAMA network open Nov 1, 2021
IMPORTANCE: Detecting cognitive decline earlier among older adults can facilitate enrollment in clinical trials and early interventions. Clinical notes in longitudinal electronic health records (EHRs) provide opportunities to detect cognitive decline...
Massachusetts Aged, 80 and over Humans Male Electronic Health Records Deep Learning Middle Aged Female Cognitive Dysfunction Risk Assessment Aged Longitudinal Studies Early Diagnosis
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Predicting physiological aging rates from a range of quantitative traits using machine learning.

Aging Oct 29, 2021
It is widely thought that individuals age at different rates. A method that measures "physiological age" or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual's risk o...
Models, Biological Female Neuroticism Genome-Wide Association Study Humans Aged Machine Learning Longitudinal Studies Aged, 80 and over Adult DNA Methylation Algorithms Young Adult Male Pulse Wave Analysis Phenotype Aging Middle Aged
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