AIMC Topic: Machine Learning

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ATR-FTIR Spectroscopy of Saliva and Machine Learning as a Screening Test for Sjögren Disease.

Analytical chemistry
Sjögren's disease is often an underdiagnosed autoimmune condition that primarily affects the exocrine glands, resulting in symptoms such as dry eyes and dry mouth. Diagnostic challenges stem from nonspecific symptoms, the absence of definitive biomar...

Using Machine Learning Methods to Examine Turnover Rates in State Health Agencies.

Journal of public health management and practice : JPHMP
CONTEXT: High turnover rates in the public health workforce pose ongoing challenges to maintain essential services and institutional knowledge. Recent studies indicate that job dissatisfaction, burnout, and structural barriers have intensified follow...

Machine learning-based management of hypertensive disorders in pregnancy: analysis of differences in key risk factors between gestational hypertension and pre-eclampsia and construction of a pre-eclampsia prediction model.

European journal of medical research
OBJECTIVES: It remains debated whether gestational hypertension (GH) and pre-eclampsia (PE) are distinct entities or different spectra of the same disease. Currently, comparative studies of risk factors for GH and PE in the same population are limite...

Relationship between C-reactive protein triglyceride glucose index and cardiovascular disease risk: a cross-sectional analysis with machine learning.

BMC medical informatics and decision making
BACKGROUND: Cardiovascular disease (CVD) continues to be a leading cause of disease burden and mortality worldwide. Identifying reliable biomarkers for CVD risk assessment is essential. This study investigates the association between the C-reactive p...

scMFF: a machine learning framework with multiple feature fusion strategies for cell type identification.

BMC bioinformatics
Accurate cell type classification is critical for downstream analysis in single-cell RNA sequencing (scRNA-seq). Most existing methods rely on a single type of feature representation-such as statistical, information theory, matrix factorization, or d...

Machine learning for genomic prediction of growth traits in aquaculture: a case study of the Australasian snapper (Chrysophrys auratus).

BMC bioinformatics
BACKGROUND: Chrysophrys auratus (family: Sparidae), commonly known as Australasian snapper, is a warm-water species being developed as a candidate for aquaculture in New Zealand. Genomic selection of elite snapper offers significant potential to acce...

A Machine Learning-Empowered Quantitative Structure-Activity Relationship Model for Predicting the Plasma Half-life of Drugs in Dogs.

The AAPS journal
Understanding a drug's plasma half-life is essential in guiding dosage regimens and optimizing therapeutic outcomes, particularly in the early stages of drug development. By using published pharmacokinetic data from Food Animal Residue Avoidance Data...

Opportunities for AI-based Model-informed Drug Development: A Comparative Analysis of NONMEM and AI-based Models for Population Pharmacokinetic Prediction.

The AAPS journal
Model-informed drug development (MIDD) plays an important role in pharmacometrics by leveraging mathematical models to optimize drug dosing strategies. Traditional methods such as nonlinear mixed effects modeling (NONMEM) have long been the gold stan...

Using machine learning for early prediction of in-hospital mortality during ICU admission in liver cancer patients.

Scientific reports
Liver cancer has a high incidence and mortality rate globally, particularly in patients requiring intensive care unit (ICU) admission. Early prediction of in-hospital mortality for these patients is crucial, yet lacking reliable tools. This study aim...

Comparative assessment of annotation tools reveals critical antimicrobial resistance knowledge gaps in Klebsiella pneumoniae.

Scientific reports
Bacterial antimicrobial resistance (AMR) poses a significant public health threat. The increase of both global awareness and affordable whole genome sequencing has yielded an ever-growing collection of bacterial genome sequence datasets and correspon...