Intensivists

AI-driven multi-omics drug repurposing nominates AZD7762 as a multitarget inhibitor of IL22RA1 and FAM221A in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is an aggressive malignancy with limited targeted treatment options and poor clinical outcomes. We developed an AI-driven multi-omics pipeline that links prognostic modeling to multitarget drug repurposing for ESCC. Summary-data-based Mendelian randomization was integrated with bulk transcriptomic datasets to identify esophageal cancer-related druggable ge...

A machine learning system enables just-in-time risk-stratified sepsis evaluations in the neonatal intensive care unit.

INTRODUCTION: NICU sepsis evaluation balances rapid antibiotic administration for mortality reduction against unnecessary treatment. Objective risk stratification optimizes resource allocation. METHODS: We retrospectively studied 191 sepsis evaluations from 136 NICU patients. Sepsis was defined as positive blood culture or clinical sepsis with ≥5 days of antibiotics. A machine learning score (POWS...

Benchmarking Multi-Modal Artificial Intelligence Models Against Student Performance: The Role of Question Characteristics in Objective Structured Practical Dental Examinations.

BACKGROUND: Objective Structured Practical Dental Examinations (OSPDEs) are high-stakes assessments in dental education. This study compared the perfo...

Explainable machine learning-driven identification of heart failure biomarkers: a multi-model feature selection approach with SHAP-based interpretability.

Heart failure (HF) remains a major clinical challenge due to its complex pathophysiology and the limitations of existing biomarkers. In this study, we...

Targeting Tau-Mitochondrial Crosstalk in Alzheimer's Disease: Integrative Multi-Omics and Artificial Intelligence-Driven Tools for the Development of Disease-Modifying Therapeutics.

Alzheimer's disease (AD) is a progressive neurodegenerative illness marked by cognitive impairment, synaptic dysfunction and neuronal death. Tau prote...

Measuring multi-site pulse transit time with an AI-enabled mmWave radar.

Pulse Transit Time (PTT) is a measure of arterial stiffness and a physiological marker associated with cardiovascular function, with an inverse relati...

Mean airway pressure as an independent risk factor for developing acute kidney injury in mechanically ventilated critically ill patients: a multi-center retrospective analysis.

Mechanical ventilation is a well-recognized risk factor for acute kidney injury (AKI), but the specific role of mean airway pressure (Pmean) remains i...

Diagnostic value of machine-learning using conventional magnetic resonance imaging markers for pediatric idiopathic intracranial hypertension: a retrospective study.

BACKGROUND: Pediatric idiopathic intracranial hypertension can be challenging to diagnose; magnetic resonance imaging (MRI) signs are considered suppo...

Machine learning-based risk prediction of 28-day mortality for sepsis patients with augmented renal clearance.

Augmented renal clearance (ARC) frequently occurs in critically ill septic patients and is known to impact survival outcomes. To address this, we aime...

Phenotype discovery and mortality prediction in sepsis-induced myocardial dysfunction: a deep learning and stratified modeling approach.

Sepsis-induced myocardial dysfunction (SIMD) is a common and heterogeneous complication in patients with sepsis and is associated with increased morta...

Interpretable machine learning to predict postoperative adverse outcomes in cardiac surgery.

BACKGROUND: Cardiac surgery is associated with significant mortality and complication risks. This study aims to develop an interpretable machine learn...

Nomogram and machine learning models for predicting the risk of delirium in ICU patients with NSTEMI.

Delirium is a frequent and clinically consequential complication among patients admitted to the intensive care unit (ICU). Early risk stratification i...

Transcriptome and experimental verification identified candidate biomarkers related to mitochondrial metabolism in sepsis-associated encephalopathy.

BACKGROUND: Mitochondrial metabolism (MM) abnormalities have been implicated in multiple diseases, but its contribution to sepsis-associated encephalo...

An explainable machine learning model for early pediatric sepsis prediction using cytokine and routine laboratory data.

BACKGROUND: Early sepsis diagnosis in children remains challenging due to nonspecific presentations. This study aimed to develop an interpretable mach...

Using Machine Learning Methodology to Identify Predictors of Non-Response to MTSS-B: a Focus on Discipline Problems and Juvenile Justice Involvement.

Multi-tiered systems of support for behavior (MTSS-B) is a widely-used tiered preventive intervention currently used in over 25,000 schools across the...

Understanding Blood-Injection-Injury Phobia: Do Not Miss the Point.

Needle and blood-injection-injury phobia is commonly encountered in the perioperative setting. It can significantly disrupt operating room throughput,...

Predicting Pediatric Mortality Across Five Intensive Care Units: Toward an Early Warning Using Machine Learning.

Despite the advances in critical care and innovations of medical technology, earlier identification of children at high mortality risk remains challen...

Assessing AI-Based Decision Support in Early Sepsis and AKI Recognition.

Sepsis and acute kidney injury (AKI) remain among the most critical conditions in acute care, associated with high morbidity and mortality. Early risk...

Interpretable Feature Extraction from Clinical Notes for Sepsis Prediction: Comparing Rule-Based, LLM, and Hybrid Approaches.

Embedding-based approaches integrate clinical notes into sepsis prediction models but produce uninterpretable representations, obscuring which clinica...

Diagnostic Accuracy of Pressure Injury Risk Assessment Tools for Critically Ill Patients: A Systematic Review and Network Meta-Analysis.

AIMS AND OBJECTIVES: To systematically evaluate and compare the diagnostic accuracy of pressure injury risk assessment tools in critically ill adult p...