Critical Care

Sepsis

Latest AI and machine learning research in sepsis for healthcare professionals.

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Critical-Care Subcategories: Sepsis
Showing 2458-2478 of 7,154 articles
Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation

Synaptic vesicle glycoprotein 2C (SV2C) is a vesicular protein enriched in dopaminergic neurons of t...

VTA dopamine neuron activity produces spatially organized stimulus and action value representations through conditioned reinforcement

What is the neural architecture by which dopamine (DA) determines choice? Reinforcement learning (RL...

Learning from human and chemical languages to predict biological function

Understanding how molecular structure encodes biological function remains a grand challenge in drug ...

The Role of Natural Language Understanding in Multimodal Video-Based Dengue Diagnosis

Detecting infection-related behavioral changes in mosquitoes from video data is challenging because ...

Unmasking Toxic Mimicry in Medical Offline Reinforcement Learning for ICU Sepsis Management via Counterfactual Clinical Audits

Offline reinforcement learning (RL) offers considerable promise for optimizing ICU treatment decisio...

Protective effects of testosterone replacement therapy on brain tumor outcomes: the Mayo Clinic Experience

BackgroundBiological sex and endocrine signaling influence cancer biology, immune response, and ther...

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AI-guided discovery of antimicrobial peptides for urinary tract infections leveraging a new catalogue of the human urinary microbiome

Urinary tract infections (UTIs) are common infections that pose a critical burden on healthcare and ...

Striatal acetylcholine enables latent-state creation during reversal learning

Like dopamine, acetylcholine is modulated in the striatum by reward-predicting cues and outcomes, ye...

CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation

Medical world models aim to learn a latent state of patient or organ physiology and a transition fun...

Interpretable gene networks from single-cell foundation models reveal conserved neurogenic dysfunction in Parkinson's disease

Interpreting large-scale singlecell transcriptomic data remains a major challenge for understanding ...

Constraints for spatially and temporally precise learning in a neural circuit model of reinforcement learning

Reinforcement learning is a key means by which animals learn appropriate actions in a given context....

Biologically Plausible Dopamine-Modulated STDP Model of Pavlovian Learning in Spiking Neural Networks

Spike-timing-dependent plasticity (STDP) and dopamine (DA) are fundamental to reward-based learning ...

The Mechanism Matters: When Knowledge Graphs Help Reinforcement Learning

Knowledge graphs (KGs) are widely used to inject prior knowledge into reinforcement learning (RL), y...

Aligning Reinforcement Learning with Clinical Practice for Safe Decision Support in Pediatric Sepsis

Offline reinforcement learning (RL) has emerged as a promising framework for clinical decision suppo...

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Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval

Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet st...

Evaluation of four large language models on complex, infectious disease case scenarios

Objectives: Large language models (LLMs) are increasingly used in medicine, but evaluation is often ...

Comorbidity Exposure-Window Definitions and Multidimensional Disparities in Long COVID Risk: Evidence from a U.S. National Cohort (2020-2024)

Long COVID (LC) affects millions of individuals worldwide, particularly those with preexisting comor...

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