Critical Care

Machine learning models predicting extubation success in mechanically ventilated patients: a systematic review and meta-analysis.

BACKGROUND: Optimal timing of extubation in mechanically ventilated patients remains a major challenge in intensive care. Machine learning (ML) models have been increasingly proposed to support clinical decision-making, yet their predictive performance and readiness for clinical application in extubation outcomes remain uncertain. This study aimed to evaluate the predictive performance and clinica...

Correction: Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

[This corrects the article DOI: 10.2196/79985.].

Deep Learning-Enabled Robust Regional Lung Function Assessment in Mechanically Ventilated AECOPD Patients Using EIT Parameters.

Reliable quantification of regional lung function via electrical impedance tomography (EIT) in mechanically ventilated AECOPD patients requires identi...

ASTHMA AT THE CROSSROADS: FROM ANTI-TNF-α SETBACKS TO NEXT-GENERATION BIOLOGICS TARGETING TYPE 1 AND TYPE 2 INFLAMMATION.

Severe asthma remains a major unmet clinical challenge due to its marked immunological heterogeneity and the limited efficacy of current therapies in ...

Fostering trust in digital respiratory medicine: the open access ERS CONNECT digital health technology repository.

Access to digital health tools is rapidly expanding, but adoption in respiratory care remains fragmented. The ERS CONNECT Repository provides a truste...

Development and validation of a novel multimodal deep neural network model based on CBC digit parameters and scattergrams for rapid hematolymphoid malignancy classification: a multicenter cohort study.

BACKGROUND: Rapid screening of hematolymphoid malignancies (HMs) by complete blood cell count (CBC) is crucial for choosing the next appropriate worku...

Cardio-respiratory deep learning model to predict hospital admission from emergency department electrocardiogram images and respiration videos: a prospective study.

BACKGROUND: Early prediction of hospital admission at the emergency department (ED) triage can improve patient flow and resource allocation. Most exis...

Reporting follow-up budgets for diagnostic and predictive AI.

OBJECTIVE: To argue that diagnostic and predictive AI should be evaluated by both classification performance and the downstream work their outputs cre...

Transformer-Based Deep Learning Model for Predicting Hemoglobin Response to Mircera® in Hemodialysis Patients.

BACKGROUND: Anemia management in hemodialysis (HD) depends on individualized erythropoiesis-stimulating agent (ESA) dosing to achieve and maintain tar...

Impact of an Artificial Intelligence-Powered Clinical Decision Support System for Acute Kidney Injury Prevention in the Intensive Care Unit: Single-Center Uncontrolled Before-and-After Implementation Study.

BACKGROUND: Acute kidney injury (AKI) is a frequent and serious complication among hospitalized patients, particularly in critical care settings, wher...

The hidden infectious disease-related sepsis risk of wildfire-specific PM2.5.

Wildfire smoke is a major source of fine particulate matter (PM2.5) and may increase vulnerability to severe infectious outcomes such as sepsis, a con...

Prediction of incident atrial fibrillation from retinal fundus images using a multimodal foundation model.

Atrial fibrillation (AF), a common cardiac arrhythmia, presents significant challenges for early detection and management due to its asymptomatic and ...

Integrated transcriptomic and experimental validation reveal that N‑acetylcysteine ameliorates acute liver failure through multi‑target synergistic regulation of pyroptosis and related signaling networks.

Pyroptosis is a major contributor to the pathophysiology of acute liver failure (ALF), a condition with high mortality. Although N‑acetylcysteine (NAC...

Explainable machine learning for the early differentiation of pediatric bronchopneumonia using routine laboratory parameters.

OBJECTIVE: Early differentiation of pediatric acute respiratory infections in outpatient and emergency settings is often hindered by nonspecific clini...

From Executor to Orchestrator: The Pharmacology Scientist in the Age of Agentic AI.

Drug development productivity has not improved despite five decades of computational advancement, with the probability that a compound entering Phase ...

Utility of E-Nose in Paediatric Respiratory Diseases Using Machine Learning Approach: A Pilot Study.

To assess the feasibility of an electronic nose (e-nose) for volatile organic compounds (VOCs) to classify respiratory diseases and establish a protoc...

Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications.

Endovascular thrombectomy (EVT) has transformed the treatment of acute ischemic stroke (AIS). However, a substantial proportion of AIS patients experi...

Feasibility of Tailoring Artificial Intelligence-Assisted Ambient Scribes for Intensive Care Unit Rounds: Algorithm Development and Validation.

BACKGROUND: The increasing documentation burden on physicians is a significant contributor to burnout and decreases in care quality. Artificial intell...

From single targets to circuits diversified discovery and development strategies towards new paradigms for non-opioid analgesics.

The long-term use of opioids for analgesia is associated with serious adverse effects such as addiction and respiratory depression, as well as a poten...

Development and multicenter validation of machine learning models for 28-day mortality in critically ill patients with acute exacerbation of chronic obstructive pulmonary disease.

BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a common critical illness in the intensive care unit (ICU) and is ...