AIMC Topic: Longitudinal Studies

Clear Filters Showing 301 to 310 of 593 articles

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
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 ...

A deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure.

Nature communications
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...

Network analysis of trauma in patients with early-stage psychosis.

Scientific reports
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 ...

Development and Validation of a Deep Learning Model for Earlier Detection of Cognitive Decline From Clinical Notes in Electronic Health Records.

JAMA network open
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...

Predicting physiological aging rates from a range of quantitative traits using machine learning.

Aging
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...

Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding.

Psychometrika
Recently, machine learning (ML) methods have been used in causal inference to estimate treatment effects in order to reduce concerns for model mis-specification. However, many ML methods require that all confounders are measured to consistently estim...

Detection of dementia on voice recordings using deep learning: a Framingham Heart Study.

Alzheimer's research & therapy
BACKGROUND: Identification of reliable, affordable, and easy-to-use strategies for detection of dementia is sorely needed. Digital technologies, such as individual voice recordings, offer an attractive modality to assess cognition but methods that co...

DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease.

NeuroImage
Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. Howe...

Quantifying changes over 1 year in motor and cognitive skill after transient ischemic attack (TIA) using robotics.

Scientific reports
Recent work has highlighted that people who have had TIA may have abnormal motor and cognitive function. We aimed to quantify deficits in a cohort of individuals who had TIA and measured changes in their abilities to perform behavioural tasks over 1 ...

Predicting pediatric anxiety from the temporal pole using neural responses to emotional faces.

Scientific reports
A prominent cognitive aspect of anxiety is dysregulation of emotional interpretation of facial expressions, associated with neural activity from the amygdala and prefrontal cortex. We report machine learning analysis of fMRI results supporting a key ...