Sepsis

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

In recent years, healthcare professionals are increasingly emphasizing on personalized and evidenc...

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...

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 resear...

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 prognosi...

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

Towards Fluorescent-Tag-Less Viral Titration: Automated Estimation of Cell-Size Distribution and Infection Level from Phase-Contrast Microscopy Using Deep Learning and Transfer Learning.

Automated detection of infected insect cells is one of the crucial tasks in the field of recombinant...

Time-varying compartmental models with neural networks for pandemic infection forecasting.

The emergence and spread of deadly pandemics has repeatedly occurred throughout history, causing wid...

PANDORA: Deep graph learning based COVID-19 infection risk level forecasting

COVID-19 as a global pandemic causes a massive disruption to social stability that threatens human...

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related g...

Application of AI in urolithiasis risk of infection: a scoping review.

INTRODUCTION: Artificial intelligence and machine learning are the new frontier in urology; they can...

Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework.

High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding ge...

Machine Learning for Clinical Decision Support of Acute Streptococcal Pharyngitis: A Pilot Study.

BACKGROUND: Group A Streptococcus (GAS) is the predominant bacterial pathogen of pharyngitis in chil...

Machine learning reveals ferroptosis features and a novel ferroptosis classifier in patients with sepsis.

OBJECTIVE: Sepsis is an organ malfunction disease that may become fatal and is commonly accompanied ...

Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population.

OBJECTIVES: Electronic health record (EHR) data may facilitate the identification of rare diseases i...

An infection prediction model developed from inpatient data can predict out-of-hospital COVID-19 infections from wearable data when controlled for dataset shift

The COVID-19 pandemic highlighted the importance of early detection of illness and the need for heal...

Where do doctors disagree? Characterizing Decision Points for Safe Reinforcement Learning in Choosing Vasopressor Treatment.

In clinical settings, domain experts sometimes disagree on optimal treatment actions. These "decisio...

Emulation of a Target Trial to Estimate the Effect of Selective Serotonin Reuptake Inhibitors on the Development of Antimicrobial-Resistant Infections using Electronic Health Record Data and Causal Machine Learning.

Antimicrobial resistance is a significant public health concern. The use of selective serotonin reup...

Enhancing Antibiotic Stewardship: A Machine Learning Approach to Predicting Antibiotic Resistance in Inpatient Care.

Antibiotics have been crucial in advancing medical treatments, but the growing threat of antibiotic ...

Evidence from Machine Learning, Diagnostic Hub Genes in Sepsis and Diagnostic Models based on Xgboost Models, Novel Molecular Models for the Diagnosis of Sepsis.

BACKGROUND: Systemic multi-organ dysfunction resulting from dysregulated immune responses in the hos...

[Research progress of artificial intelligence technology in early diagnosis of sepsis].

Sepsis is caused by infection, which can ultimately lead to multiple organ dysfunction and even life...

DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray images.

BACKGROUND: COVID-19 needs to be diagnosed and staged to be treated accurately. However, prior studi...