AIMC Topic: Machine Learning

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Machine learning approach for dosage individualization of azithromycin in children with community-acquired pneumonia.

British journal of clinical pharmacology
AIMS: The uncertainty about the efficacy and safety of currently used azithromycin dosing regimens in children warrants individualized therapy. The area under the plasma concentration-time curve over 24 h (AUC) of azithromycin correlates best with it...

Early obesity risk prediction via non-dietary lifestyle factors using machine learning approaches.

Clinical obesity
Obesity poses a significant health threat, contributing to the development of noncommunicable diseases (NCDs). Early identification of individuals at higher risk for obesity is crucial for implementing effective prevention strategies. This study expl...

A machine learning model to predict intradialytic hypotension in pediatric continuous kidney replacement therapy.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Intradialytic hypotension (IDH) is associated with mortality in adults undergoing intermittent hemodialysis, but this relationship is unclear in critically ill children receiving continuous kidney replacement therapy (CKRT). We aim to eva...

[Artificial intelligence in advanced surgical training : An overview].

Unfallchirurgie (Heidelberg, Germany)
The integration of artificial intelligence (AI) into medical education and advanced training is gaining in importance. Especially in advanced surgical training AI offers potential to support both theoretical and practical learning processes. Applicat...

Can small dense LDL cholesterol be estimated from the lipid profile?

Current opinion in lipidology
PURPOSE OF REVIEW: Small dense low-density lipoprotein cholesterol (sdLDL-C) is recognized for its strong atherosclerogenic potential. However, its direct measurement remains impractical in clinical settings due to its high cost, time constraints, an...

A framework predicting removal efficacy of antibiotic resistance genes during disinfection processes with machine learning.

Journal of hazardous materials
Disinfection has been applied widely for the removal of antibiotic resistance genes (ARGs) to curb the spread of antibiotic resistance. Quantitative polymerase chain reaction (qPCR) is the most used method to quantify the damage of DNA thus calculati...

Characterization of Enterocytozoon hepatopenaei infection stages in shrimp using machine learning and gene network analysis.

Journal of invertebrate pathology
Enterocytozoon hepatopenaei (EHP), causing hepatopancreatic microsporidiosis (HPM), significantly impacts Litopenaeus vannamei, leading to economic losses. Using bioinformatics and machine learning, this study characterized EHP infection stages and h...

Learning-Based Models for Predicting IVIG Resistance and Coronary Artery Lesions in Kawasaki Disease: A Review of Technical Aspects and Study Features.

Paediatric drugs
Kawasaki disease (KD) is a common pediatric vasculitis, with coronary artery lesions (CALs) representing its most severe complication. Early identification of high-risk patients, including those with disease resistant to first-line treatments, is ess...

Predicting high-risk factors for postoperative inadequate analgesia and adverse reactions in cesarean delivery surgery: a prospective study.

International journal of surgery (London, England)
BACKGROUND: Early identification of high-risk factors for inadequate analgesia and adverse reactions in obstetric patients is critical for improving outcomes. This study developed a machine learning model to predict these factors and optimize anesthe...

Radiomics across modalities: a comprehensive review of neurodegenerative diseases.

Clinical radiology
Radiomics allows extraction from medical images of quantitative features that are able to reveal tissue patterns that are generally invisible to human observers. Despite the challenges in visually interpreting radiomic features and the computational ...