Infectious Disease

Machine learning prediction models for multidrug-resistant organism infections in ICU ventilator-associated pneumonia patients: Analysis using the MIMIC-IV database.

OBJECTIVE: This study aims to construct and compare four machine learning models using the MIMIC-IV database to identify high-risk factors for multidrug-resistant organism (MDRO) infection in Ventilator-associated pneumonia (VAP) patients.

Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post-COVID-19 Condition: Protocol for an Observational Study.

BACKGROUND: Post-COVID-19 condition is emerging as a new epidemic, characterized by the persistence of COVID-19 symptoms beyond 3 months, and is anticipated to substantially alter the lives of millions of people globally. Patients with severe episodes of COVID-19 are significantly more likely to be hospitalized in the following months. The pathophysiological mechanisms for delayed complications ar...

Constructing an early warning model for elderly sepsis patients based on machine learning.

Sepsis is a serious threat to human life. Early prediction of high-risk populations for sepsis is necessary especially in elderly patients. Artificial...

Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study.

BACKGROUND: Pertussis is a highly contagious respiratory disease. Even though vaccination has reduced the incidence, cases have resurfaced in certain ...

Large Language Model-Driven Knowledge Graph Construction in Sepsis Care Using Multicenter Clinical Databases: Development and Usability Study.

BACKGROUND: Sepsis is a complex, life-threatening condition characterized by significant heterogeneity and vast amounts of unstructured data, posing s...

Deep neural networks excel in COVID-19 disease severity prediction-a meta-regression analysis.

COVID-19 is a disease in which early prognosis of severity is critical for desired patient outcomes and for the management of limited resources like i...

Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines.

OBJECTIVE: Bloodstream infection (BSI) is a significant cause of mortality in patients with hematologic malignancies(HMs), particularly amid rising an...

Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach.

BACKGROUND: Monkeypox, a viral zoonotic disease, is an emerging global health concern, with rising incidence and outbreaks extending beyond its endemi...

Machine Learning-Guided Screening and Molecular Docking for Proposing Naturally Derived Drug Candidates Against MERS-CoV 3CL Protease.

In this study, we utilized machine learning techniques to identify potential inhibitors of the MERS-CoV 3CL protease. Among the models evaluated, the ...

Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis.

: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are...

Preliminary evaluation of ChatGPT model iterations in emergency department diagnostics.

Large language model chatbots such as ChatGPT have shown the potential in assisting health professionals in emergency departments (EDs). However, the ...

Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning.

New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Mode...

Deep learning image analysis for continuous single-cell imaging of dynamic processes in Plasmodium falciparum-infected erythrocytes.

Continuous high-resolution imaging of the disease-mediating blood stages of the human malaria parasite Plasmodium falciparum faces challenges due to p...

Application of explainable machine learning in the production of pullulan by Aureobasidium pullulans CGMCCNO.7055.

The application of machine learning in pullulan biofermentation has demonstrated significant potential. Explainable machine learning enhances model tr...

Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading global health challenge, especially in low-resource settings. Accurate diag...

Leveraging artificial intelligence to assess the impact of COVID-19 on the teacher-student relationship in higher education.

The teacher-student relationship has far-reaching implications for educational outcomes at the tertiary level. Teachers contribute to students' succes...

Weighted-VAE: A deep learning approach for multimodal data generation applied to experimental T. cruzi infection.

Chagas disease (CD), caused by the protozoan parasite Trypanosoma cruzi (T. cruzi), represents a major public health concern in most of the American c...

TCDE-Net: An unsupervised dual-encoder network for 3D brain medical image registration.

Medical image registration is a critical task in aligning medical images from different time points, modalities, or individuals, essential for accurat...

Artificial intelligence-assisted magnetic resonance lymphography for evaluation of micro- and macro-sentinel lymph node metastasis in breast cancer.

Contrast-enhanced magnetic resonance lymphography (CE-MRL) plays a crucial role in preoperative diagnostic for evaluating tumor metastatic sentinel ly...

Genetic algorithm-optimized artificial neural network for multi-objective optimization of biomass and exopolysaccharide production by Haloferax mediterranei.

Microbial production of industrially important exopolysaccharide (EPS) from extremophiles has several advantages. In this study, key media components ...