AIMC Topic: Machine Learning

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Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort.

Endocrine
BACKGROUND: The identification of associated overweight risk factors is crucial to future health risk predictions and behavioral interventions. Several consensus problems remain in machine learning, such as cross-validation, and the resulting model m...

Confound-leakage: confound removal in machine learning leads to leakage.

GigaScience
BACKGROUND: Machine learning (ML) approaches are a crucial component of modern data analysis in many fields, including epidemiology and medicine. Nonlinear ML methods often achieve accurate predictions, for instance, in personalized medicine, as they...

Predicting Persistent Opioid Use after Hand Surgery: A Machine Learning Approach.

Plastic and reconstructive surgery
BACKGROUND: The aim of this study was to evaluate the use of machine learning to predict persistent opioid use after hand surgery.

A scoping review of methodologies for applying artificial intelligence to physical activity interventions.

Journal of sport and health science
PURPOSE: This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence (AI) applications in physical activity (PA) interventions; introduce them to prevalent machine learning (ML), deep learning (DL), an...

Machine Learning: A New Approach for Dose Individualization.

Clinical pharmacology and therapeutics
The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its earl...

The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies.

Obstetrics and gynecology clinics of North America
Artificial intelligence (AI) and machine learning, the form most commonly used in medicine, offer powerful tools utilizing the strengths of large data sets and intelligent algorithms. These systems can help to revolutionize delivery of treatments, ac...

Approaches and Limitations of Machine Learning for Synthetic Ultrasound Generation: A Scoping Review.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
This scoping review examines the emerging field of synthetic ultrasound generation using machine learning (ML) models in radiology. Nineteen studies were analyzed, revealing three primary methodological strategies: unconditional generation, condition...

Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques.

Computers in biology and medicine
Bronchial asthma is a prevalent non-communicable disease among children. The study collected clinical data from 390 children aged 4-17 years with asthma, with or without rhinitis, who received allergen immunotherapy (AIT). Combining these data, this ...

Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors.

Methods (San Diego, Calif.)
Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but i...