AIMC Topic: Machine Learning

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Parametric cushioning lattice insole based on finite element method and machine learning: A preliminary computational analysis.

Journal of biomechanics
The cushioning performance of insole has always been a critical consideration in its design. While the development of intelligent methods and the emergence of additive manufacturing (AM) technology have enhanced design freedom and convenience, a stan...

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.

Journal of biomedical informatics
OBJECTIVE: Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning framework to identify subpheno...

Enhancing data quality in medical concept normalization through large language models.

Journal of biomedical informatics
OBJECTIVE: Medical concept normalization (MCN) aims to map informal medical terms to formal medical concepts, a critical task in building machine learning systems for medical applications. However, most existing studies on MCN primarily focus on mode...

Artificial intelligence applied to epilepsy imaging: Current status and future perspectives.

Revue neurologique
In recent years, artificial intelligence (AI) has become an increasingly prominent focus of medical research, significantly impacting epileptology as well. Studies on deep learning (DL) and machine learning (ML) - the core of AI - have explored their...

DALL-M: Context-aware clinical data augmentation with large language models.

Computers in biology and medicine
X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases, necessitating the integration of structured clinical fea...

Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence.

Advanced materials (Deerfield Beach, Fla.)
Intelligent wearable sensors, empowered by machine learning and innovative smart materials, enable rapid, accurate disease diagnosis, personalized therapy, and continuous health monitoring without disrupting daily life. This integration facilitates a...

Machine Learning-Enhanced Prediction for Soil-to-Air VOC Emission and Environmental Impact Pertaining Contaminated Fractured Aquifers.

Environmental science & technology
How to scientifically and efficiently quantify the impact and hazards of volatile organic compounds (VOCs) pollution and volatilization from complex groundwater systems on surface air environments is a critical environmental issue. This paper employe...

AI-Enabled Screening for Retinopathy of Prematurity in Low-Resource Settings.

JAMA network open
IMPORTANCE: Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. If detected and treated early, ROP-associated blindness is preventable; however, identifying patients who might respond to treatment requi...

Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach.

Toxins
The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation strategies, but also to alleviate environmental pollution. In the present study, a novel machine le...

Length Estimation of Pneumatic Artificial Muscle with Optical Fiber Sensor Using Machine Learning.

Sensors (Basel, Switzerland)
A McKibben artificial muscle is a soft actuator driven by air pressure, characterized by its flexibility, lightweight design, and high power-to-weight ratio. We have developed a smart artificial muscle that is capable of sensing its motion. To enable...