AIMC Topic: Machine Learning

Clear Filters Showing 27751 to 27760 of 34417 articles

Addressing bias in biomarker discovery for inflammatory bowel diseases: A multi-faceted analytical approach.

International immunopharmacology
Xiang-Guang et al. investigate the identification of novel biomarkers linked to M1 macrophage infiltration in inflammatory bowel diseases (IBD). Utilizing advanced bioinformatics and machine learning techniques, the researchers developed predictive m...

A meta-learning method for estimation of causal excursion effects to assess time-varying moderation.

Biometrics
Advances in wearable technologies and health interventions delivered by smartphones have greatly increased the accessibility of mobile health (mHealth) interventions. Micro-randomized trials (MRTs) are designed to assess the effectiveness of the mHea...

MLDAAPP: machine learning data acquisition for assessing population phenotypes.

G3 (Bethesda, Md.)
Collecting phenotypic data from many individuals is critical to answering fundamental biological questions, particularly in genetics. Yet, whole organismal phenotypic data are still often collected manually; limiting the scale of data generation, pre...

Limitations of current machine learning models in predicting enzymatic functions for uncharacterized proteins.

G3 (Bethesda, Md.)
Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein "unknome." This large knowledge shortfall is one of the final frontiers of biology. Machine learning (ML) approaches are enticing...

Identification of key factors and explainability analysis for surgical decision-making in hepatic alveolar echinococcosis assisted by machine learning.

World journal of gastroenterology
BACKGROUND: Echinococcosis, caused by Echinococcus parasites, includes alveolar echinococcosis (AE), the most lethal form, primarily affecting the liver with a 90% mortality rate without prompt treatment. While radical surgery combined with antiparas...

Machine Learning-based Prediction of Active Tuberculosis in People With HIV Using Clinical Data.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
BACKGROUND: Coinfections of Mycobacterium tuberculosis (MTB) and human immunodeficiency virus (HIV) impose a substantial global health burden. Patients with MTB infection face a heightened risk of progression to incident active TB, which preventive t...

Machine Learning Algorithms for Predicting Urinary Tract Infections: Integration of Demographic Data and Dipstick Reflectance Results.

Clinical chemistry
BACKGROUND: Urinary tract infections (UTIs) are among the most common infections encountered in healthcare settings. Current diagnostic practices often require 24-48 h due to the time needed for culture results. Given that 70%-80% of cultures return ...

At-home wearables and machine learning capture motor impairment and progression in adult ataxias.

Brain : a journal of neurology
A significant barrier to developing disease-modifying therapies for spinocerebellar ataxias (SCAs) and multiple system atrophy of the cerebellar type (MSA-C) is the scarcity of tools to measure disease progression sensitively in clinical trials. Wear...

Show and tell: A critical review on robustness and uncertainty for a more responsible medical AI.

International journal of medical informatics
This critical review explores two interrelated trends: the rapid increase in studies on machine learning (ML) applications within health informatics and the growing concerns about the reproducibility of these applications across different healthcare ...