AIMC Topic: Machine Learning

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Development of a Diagnostic Prediction Model for Post-Stroke Cognitive Impairment in Acute Large Vessel Occlusion Stroke Using Multimodal MRI and PET/CT: A Study Protocol.

Brain and behavior
OBJECTIVE: Stroke is a leading cause of morbidity and disability worldwide. Post-stroke cognitive impairment (PSCI) significantly affects long-term prognosis in acute anterior circulation large-vessel occlusion stroke (LVO-AIS). This study aims to de...

Attention Regulation Among Sleep-Deprived Air-Force Pilots.

Journal of neuroscience research
Short sleep duration is associated with adverse physical and mental events. However, it is quite challenging to objectively quantify its impact on human cognitive performance. Thus, we aim to examine the effects of sleep deprivation on physiological ...

Do Transformers and CNNs Learn Different Concepts of Brain Age?

Human brain mapping
"Predicted brain age" refers to a biomarker of structural brain health derived from machine learning analysis of T1-weighted brain magnetic resonance (MR) images. A range of machine learning methods have been used to predict brain age, with convoluti...

Brain Aging in Patients With Cardiovascular Disease From the UK Biobank.

Human brain mapping
The brain undergoes complex but normal structural changes during the aging process in healthy adults, whereas deviations from the normal aging patterns of the brain can be indicative of various conditions as well as an increased risk for the developm...

A Bayesian Approach to the G-Formula via Iterative Conditional Regression.

Statistics in medicine
In longitudinal observational studies with time-varying confounders, the generalized computation algorithm formula (g-formula) is a principled tool to estimate the average causal effect of a treatment regimen. However, the standard non-iterative g-fo...

Integrating Multiple Data Sources With Interactions in Multi-Omics Using Cooperative Learning.

Statistics in medicine
Modeling with multiomics data presents multiple challenges, such as the high dimensionality of the problem ( ), the presence of interactions between features, and the need for integration between multiple data sources. We establish an interaction mo...

A Machine Learning Algorithm to Estimate the Probability of a True Scaphoid Fracture After Wrist Trauma.

The Journal of hand surgery
PURPOSE: To identify predictors of a true scaphoid fracture among patients with radial wrist pain following acute trauma, train 5 machine learning (ML) algorithms in predicting scaphoid fracture probability, and design a decision rule to initiate adv...

Technical Note: Using Machine Learning to Predict Locomotor Behavior in Great Apes and Humans From Femur Metaphyseal Shape.

American journal of biological anthropology
OBJECTIVES: The morphology of the hominoid distal femoral metaphyseal surface has been demonstrated to reflect locomotor behavior throughout ontogeny. Here, we quantify metaphyseal surface morphology to evaluate its predictive relationship to locomot...

Machine Learning Insights Into Amputation Risk: Evaluating Wound Classification Systems in Diabetic Foot Ulcers.

International wound journal
This study compares the performance of various wound classification systems to determine which system most effectively predicts amputation risk in diabetic foot ulcer (DFU) patients. Additionally, it identifies the key clinical and socioeconomic fact...

Clinical Trial Design Approach to Auditing Language Models in Health Care Setting.

JCO clinical cancer informatics
PURPOSE: Rapid advancements in natural language processing have led to the development of sophisticated language models. Inspired by their success, these models are now used in health care for tasks such as clinical documentation and medical record c...