Sepsis

Machine learning identification of maternal inflammatory response and histologic choroamnionitis from placental membrane whole slide images

The placenta forms a critical barrier to infection through pregnancy, labor and, delivery. Inflammatory processes in the placenta have short-term, and long-term consequences for offspring health. Digital pathology and machine learning can play an important role in understanding placental inflammation, and there have been very few investigations into methods for predicting and understanding Mater...

Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?

OBJECTIVE: To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches.

Machine learning-driven in-hospital mortality prediction in HIV/AIDS patients with infection: a single-centred retrospective study.

() is a widely disseminated betaherpesvirus that typically induces latant infections. In immunocompromised populations, especially transplant and HI...

Assessing Concordance between RNA-Seq and NanoString Technologies in Ebola-Infected Nonhuman Primates Using Machine Learning

This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates...

Bayesian Approaches for Revealing Complex Neural Network Dynamics in Parkinson's Disease

Parkinson's disease (PD) belongs to the class of neurodegenerative disorders that affect the central nervous system. It is usually defined as the gr...

ZIF-90 treats fungal keratitis by promoting macrophage apoptosis and inhibiting inflammatory response

Fungal keratitis is a severe vision-threatening corneal infection with a prognosis influenced by fungal virulence and the host's immune defense mech...

Off-Policy Selection for Initiating Human-Centric Experimental Design

In human-centric tasks such as healthcare and education, the heterogeneity among patients and students necessitates personalized treatments and inst...

Contrastive learning of cell state dynamics in response to perturbations

We introduce DynaCLR, a self-supervised framework for modeling cell dynamics via contrastive learning of representations of time-lapse datasets. Liv...

Unveiling the glycolysis in sepsis: Integrated bioinformatics and machine learning analysis identifies crucial roles for IER3, DSC2, and PPARG in disease pathogenesis.

Sepsis, a multifaceted syndrome driven by an imbalanced host response to infection, remains a significant medical challenge. At its core lies the pivo...

A generalizable framework for unlocking missing reactions in genome-scale metabolic networks using deep learning

Incomplete knowledge of metabolic processes hinders the accuracy of GEnome-scale Metabolic models (GEMs), which in turn impedes advancements in syst...

Discrimination vs. Generation: The Machine Learning Dichotomy for Dopaminergic Hit Discovery

Virtual screening plays a pivotal role in early drug discovery, traditionally dominated by physics-based methods. While these approaches offer detai...

3D Topological Modeling and Multi-Agent Movement Simulation for Viral Infection Risk Analysis

In this paper, a method to study how the design of indoor spaces and people's movement within them affect disease spread is proposed by integrating ...

Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning

Lung cancer remains a leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) being the most common subtype. This s...

An Overview of Explainable AI Studies in the Prediction of Sepsis Onset and Sepsis Mortality.

Explainable artificial intelligence (AI) focuses on developing models and algorithms that provide transparent and interpretable insights into decision...

SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing

Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the surviva...

Mapping Patient Trajectories: Understanding and Visualizing Sepsis Prognostic Pathways from Patients Clinical Narratives

In recent years, healthcare professionals are increasingly emphasizing on personalized and evidence-based patient care through the exploration of pr...

Identification and validation of potential genes for the diagnosis of sepsis by bioinformatics and 2-sample Mendelian randomization study.

This integrated study combines bioinformatics, machine learning, and Mendelian randomization (MR) to discover and validate molecular biomarkers for se...

Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model

Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for vari...

Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis

We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency depa...

Response to the letter to the editor regarding "Assisting the Infection Preventionist: Use of Artificial Intelligence for Healthcare-Associated Infection Surveillance".