AIMC Topic: Machine Learning

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Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development.

Current medical research and opinion
Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vi...

High-Throughput Screening and Prediction of Nucleophilicity of Amines Using Machine Learning and DFT Calculations.

Journal of chemical information and modeling
Nucleophilic index () as a significant parameter plays a crucial role in screening of amine catalysts. Indeed, the quantity and variety of amines are extensive. However, only limited amines exhibit an value exceeding 4.0 eV, rendering them potential...

Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks.

Sensors (Basel, Switzerland)
Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data privacy. In the healthcare domain, FL enables collaborative training among diverse medical devices and ...

Identification of Psychological Treatment Dropout Predictors Using Machine Learning Models on Italian Patients Living with Overweight and Obesity Ineligible for Bariatric Surgery.

Nutrients
According to the main international guidelines, patients with obesity and psychiatric/psychological disorders who cannot be addressed to surgery are recommended to follow a nutritional approach and a psychological treatment. A total of 94 patients (T...

Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches.

International journal of molecular sciences
Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the m...

The application of machine learning methods for predicting the progression of adolescent idiopathic scoliosis: a systematic review.

Biomedical engineering online
Predicting curve progression during the initial visit is pivotal in the disease management of patients with adolescent idiopathic scoliosis (AIS)-identifying patients at high risk of progression is essential for timely and proactive interventions. Bo...

Prediction of 30-day mortality for ICU patients with Sepsis-3.

BMC medical informatics and decision making
BACKGROUND: There is a growing demand for advanced methods to improve the understanding and prediction of illnesses. This study focuses on Sepsis, a critical response to infection, aiming to enhance early detection and mortality prediction for Sepsis...

A risk prediction model based on machine learning algorithm for parastomal hernia after permanent colostomy.

BMC medical informatics and decision making
OBJECTIVE: To develop a machine learning-based risk prediction model for postoperative parastomal hernia (PSH) in colorectal cancer patients undergoing permanent colostomy, assisting nurses in identifying high-risk groups and devising preventive care...

Factors affecting biochemical pregnancy loss (BPL) in preimplantation genetic testing for aneuploidy (PGT-A) cycles: machine learning-assisted identification.

Reproductive biology and endocrinology : RB&E
PURPOSE: To determine the factors influencing the likelihood of biochemical pregnancy loss (BPL) after transfer of a euploid embryo from preimplantation genetic testing for aneuploidy (PGT-A) cycles.

Unbiased identification of risk factors for invasive Escherichia coli disease using machine learning.

BMC infectious diseases
BACKGROUND: Invasive Escherichia coli disease (IED), also known as invasive extraintestinal pathogenic E. coli disease, is a leading cause of sepsis and bacteremia in older adults that can result in hospitalization and sometimes death and is frequent...