Sepsis

PROFIS: Design of Target-Focused Libraries by Probing Continuous Fingerprint Space with Recurrent Neural Networks.

This study introduces PROFIS, a new generative model capable of the design of structurally novel and target-focused compound libraries. The model relies on a recurrent neural network that was trained to decode embedded molecular fingerprints into SMILES strings. To identify potential novel ligands, a biological activity predictor is first trained on the low-dimensional fingerprint embedding space,...

Mitigating Bias in Machine Learning Models with Ethics-Based Initiatives: The Case of Sepsis.

This paper discusses ethics-based strategies for mitigating bias in machine learning models used to predict sepsis onset. The first part discusses how various kinds of bias and their potential synergies can reduce predictive accuracy, especially as those biases derive from social determinants of health (SDOHs) and from the design and construction of the predictive model. The second part of the ess...

Investigation of Bleeding Disorders: When and How Should We Test Platelet Functions?

Inherited platelet disorders (IPDs) are rare conditions with diverse underlying pathophysiology which should be suspected in patients presenting with ...

Prescriptive analytics decision-making system for cardiovascular disease prediction in long COVID patients using advanced reinforcement learning algorithms.

In recent years Covid-19 impact is causing unprecedented difficulties worldwide, affecting lifestyle choices. The post-pandemic era has made this even...

A novel scheme for non-invasive drug delivery with a magnetically controlled drug delivering capsule endoscope.

There is a lack of effective means for precise drug delivery of gastrointestinal diseases. Herein we report a novel magnetically controlled drug deliv...

Antimicrobial Susceptibility Profiles of Isolates from Clinical Cases of Ducks in Hungary Between 2022 and 2023.

: Antimicrobial resistance (AMR) poses a growing threat to veterinary medicine and food safety. This study examines antibiotic resistance patterns in...

Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage.

Patients with intracerebral hemorrhage (ICH) are highly susceptible to sepsis. This study evaluates the efficacy of machine learning (ML) models in pr...

Antagonistic coinfection in rock-paper-scissors models during concurrent epidemics

We investigate the dynamics of dual disease epidemics within the spatial rock-paper-scissors model. In this framework, individuals from all species ...

Identification of Hub Genes and Key Pathways Associated with Sepsis Progression Using Weighted Gene Co-Expression Network Analysis and Machine Learning.

Sepsis is a life-threatening condition driven by dysregulated immune responses, resulting in organ dysfunction and high mortality rates. Identifying k...

argNorm: normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO).

SUMMARY: Currently available and frequently used tools for annotating antibiotic resistance genes (ARGs) in genomes and metagenomes provide results us...

Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study.

Sepsis, septic shock, and cardiogenic shock are life-threatening conditions associated with high mortality rates, but differentiating them is complex ...

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos

Parkinson's disease (PD) is a neurodegenerative disorder, manifesting with motor and non-motor symptoms. Depressive symptoms are prevalent in PD, af...

Early Prediction of Sepsis: Feature-Aligned Transfer Learning

Sepsis is a life threatening medical condition that occurs when the body has an extreme response to infection, leading to widespread inflammation, o...

Enhancing LLMs' Clinical Reasoning with Real-World Data from a Nationwide Sepsis Registry

Although large language models (LLMs) have demonstrated impressive reasoning capabilities across general domains, their effectiveness in real-world ...

Probability Score for the Diagnosis of Periprosthetic Joint Infection: Development and Validation of a Practical Multi-analyte Machine Learning Model.

Background and objective The diagnosis of periprosthetic joint infection (PJI) relies on established criteria-based systems requiring interpretation a...

Generative AI mitigates representation bias and improves model fairness through synthetic health data.

Representation bias in health data can lead to unfair decisions and compromise the generalisability of research findings. As a consequence, underrepre...

Exploring genetic and immune cell dynamics in systemic lupus erythematosus patients with Epstein-Barr virus infection via machine learning.

OBJECTIVES: EBV is a widespread virus implicated in various diseases, including SLE. However, the specific genes and pathways altered in SLE patients ...

From Lab to Wrist: Bridging Metabolic Monitoring and Consumer Wearables for Heart Rate and Oxygen Consumption Modeling

Understanding physiological responses during running is critical for performance optimization, tailored training prescriptions, and athlete health m...

Model uncertainty quantification using feature confidence sets for outcome excursions

When implementing prediction models for high-stakes real-world applications such as medicine, finance, and autonomous systems, quantifying predictio...

Deciphering the unique dynamic activation pathway in a G protein-coupled receptor enables unveiling biased signaling and identifying cryptic allosteric sites in conformational intermediates

Neurotensin receptor 1 (NTSR1), a member of the Class A G protein-coupled receptor superfamily, plays an important role in modulating dopaminergic n...