Intensivists

Predicting Postoperative Outcomes in Pediatric Ureteroscopy Using Machine Learning and Explainable AI-EAU Endourology Vision-AI Study.

AIM OF THE STUDY: This study aimed to evaluate the performance of machine learning (ML) algorithms integrated with explainable artificial intelligence (XAI) techniques in predicting outcomes following flexible ureteroscopy (fURSL) in children. By identifying significant preoperative predictors, the goal was to improve individualized surgical risk assessment and planning. MATERIALS AND METHODS: A r...

AI Methods for Implementation Science (AIM-IS): developing a framework, toolkit, and reporting standard for the responsible use of AI in implementation practice and research.

BACKGROUND: Artificial intelligence (AI), including machine learning, natural language processing, and large language models, may support implementation practice and research in tasks such as evidence synthesis, determinant assessment, strategy selection, monitoring, adaptation, and theory development. However, these applications of AI do not form a single, uniform category. They span a continuum ...

Integrated multi-omics and machine learning identify Casp8 and Pycard as central regulators of pyroptosis and necroptosis in spinal cord injury.

Programmed cell death pathways exacerbate secondary damage after spinal cord injury, yet their shared regulators and tractable therapeutic points rema...

Comparison of AI-based Chatbot Performance in Analyzing Clinical Scenarios versus Medical Residents: A Novel Approach in Chest Diseases Education.

OBJECTIVE: Rapid advancements in artificial intelligence (AI) technologies offer new opportunities in medical education. The aim of this study is to c...

Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers.

Hierarchical classification aims to sort the object into a hierarchical structure of categories. For example, a bird can be categorized according to a...

Interpretable machine learning for early prediction of sepsis-induced coagulopathy: a multicenter retrospective development and validation study.

Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.

Performance of Machine Learning Models for Sepsis and Stroke Detection Using EMS Data.

OBJECTIVES: Early sepsis and stroke recognition by emergency medical services (EMS) improves triage, treatment, and patient outcomes. Machine learning...

Explainable machine learning for predicting mechanical ventilation in Alzheimer's patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV.

Alzheimer's disease (AD) patients are particularly vulnerable to pneumonia and subsequent respiratory failure due to neurodegeneration-induced dysphag...

The multi-target cardiac protection mechanism of butylphthalide and AI-driven precision medicine: From molecular basis to clinical translation.

Butylphthalide (NBP) is a lipophilic small-molecule drug characterized by its multi-target and multi-pathway regulatory properties. Initially employed...

Association between aspirin use and mortality in critically ill patients with heart failure: a retrospective study using the MIMIC-IV database.

BACKGROUND: Despite the established antithrombotic benefits of aspirin in cardiovascular disease, its efficacy remains controversial for critically il...

Development and validation of a precision treatment rules for first-line antipsychotic recommendations in first episode psychosis jointly incorporating effectiveness, side effects and patient preferences.

Selecting first-line antipsychotic medication for first episode of psychosis patients is a very challenging task requiring the clinicians to empirical...

Association between the monocyte-to-lymphocyte ratio and 28-day all-cause mortality in sepsis-associated delirium patients: a retrospective study and machine learning.

Computed tomography in ARDS, from morphological insights to AI-powered multi-modal analysis: a narrative review.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a critical clinical condition characterized by acute respiratory failure and high mortality....

Consciousness Monitoring and Outcome Prediction Using EEG Connectivity in Severe Traumatic Brain Injury.

Neurological prognostication of patients in post-traumatic coma remains challenging due to the paucity of reliable markers in the acute phase. We aime...

The landscape of artificial intelligence-enabled medical devices in the EU and the US intended for intensive care units.

Artificial intelligence (AI) is increasingly used in healthcare, yet translation into routine intensive care units (ICU) practice remains slow. Throug...

Machine learning for early detection and prediction of sepsis: explainability and key sepsis biomarkers representation-A systematic review.

OBJECTIVE: To systematically review machine learning-based sepsis prediction studies, examining model explainability and the extent to which explanati...

Multicenter validation and updating of the ELDER-ICU model for severity assessment in elderly critical illness.

The ELDER-ICU model, a machine learning tool for predicting in-hospital mortality in critically ill older adults ( ≥ 65 years), was externally validat...

An interpretable deep learning framework for predictive modeling of postoperative infections in ICU patients.

A significant proportion of intensive care unit (ICU) patients undergo surgical procedures, and some may develop postoperative infections. Accurately ...

Equiflow: An open-source software package for evaluating changes in cohort composition.

Clinical research studies routinely apply exclusion criteria and data preprocessing steps that can substantially alter dataset composition, potentiall...