Critical Care

Evaluating the efficacy of large language models in predicting intensive care unit admission needs.

BACKGROUND: Timely identification and transfer of critically ill patients to intensive care units (ICUs) are crucial to reducing morbidity and mortality. Delayed ICU admission is linked to higher mortality, emphasizing the need for efficient prediction systems. Artificial intelligence (AI) has shown promise in enhancing clinical decision-making. This study evaluates the efficacy of ChatGPT and Gem...

Lung scintigraphy for the diagnosis of acute pulmonary embolism.

Pulmonary embolism (PE) remains a major diagnostic challenge due to its potentially life-threatening nature and the clinical burden associated with anticoagulation therapy. While computed tomography pulmonary angiography (CTPA) has become the dominant imaging modality for suspected PE, limitations including radiation exposure - particularly to breast tissue-iodinated contrast administration, and c...

Domain Knowledge is Power: Leveraging Physiological Priors for Self-Supervised Representation Learning in Electrocardiography.

Electrocardiograms (ECGs) play a crucial role in diagnosing heart conditions; however, the effectiveness of artificial intelligence (AI)-based ECG ana...

A machine learning-based prediction model for poor prognosis in sepsis using lymphocyte count: a national, multicenter prospective cohort.

Sepsis-induced immunosuppression leads to poor prognosis. Circulating lymphocyte count (LC), as an easily accessible clinical marker, closely reflects...

Assessing in-hospital mortality risk in ICU lung cancer patients using machine learning: An analysis based on the MIMIC-IV database.

BACKGROUND: Patients with advanced lung cancer admitted to the intensive care unit (ICU) face a substantially elevated risk of in-hospital mortality. ...

Integrative analysis identifies lactylation-related biomarkers in sepsis via bioinformatics and machine learning.

BACKGROUND: Protein lactylation is a novel post-translational modification driven by lactate and has emerged as a critical link between cellular metab...

The Contribution of the Locus Ceruleus-Norepinephrine System to the Coupling between Pupil-Linked Arousal and Cortical State.

Understanding how pupil-linked arousal couples with cortical state is crucial for uncovering the neural mechanisms underlying brain state-dependent co...

Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU.

BACKGROUND: Mechanical ventilation is a critical life support technology in the intensive care unit. However, the weaning process remains complex, mak...

Dual Ontology-enhanced Clinical Decision Learning for First-admission Mortality Prediction.

Time-series based deep learning methods have significantly improved performance of predictive healthcare tasks on electronic health records (EHR) data...

Multidisciplinary artificial intelligence systems versus single-model approaches for the diagnosis and management of ileus and volvulus.

BACKGROUND AND AIMS: The accurate and timely diagnosis of ileus versus volvulus is essential in emergency care, as treatment choices directly influenc...

cervical nuclei segmentation through synergic conditional generative adversarial network in cervical smear images.

Cervical nuclei segmentation is critical for the early detection and accurate diagnosis of cervical cancer. However, this task is challenging due to t...

Lifespan Pancreas Morphology for Control Versus Type 2 Diabetes Using AI on Largescale Clinical Imaging.

Understanding how pancreas size and shape change with normal aging is critical for establishing a baseline to detect deviations in type 2 diabetes and...

Goals of Care Transitions following Postoperative Undertriage.

BACKGROUND: This study tests the hypothesis that postoperative undertriage of high-acuity patients to hospital floor units is associated with new post...

Artificial intelligence in lung scintigraphy.

Lung scintigraphy is a common nuclear medicine procedure for evaluating pulmonary perfusion and ventilation, particularly in the diagnosis of pulmonar...

Subphenotype Identification for Sepsis-Associated Acute Kidney Injury Using Graph Bidirectional Mamba Networks.

Sepsis-associated acute kidney injury (SA-AKI) is a heterogeneous clinical syndrome and a leading cause of mortality in intensive care units (ICUs). I...

Unbiased clustering of acute-on-chronic liver failure patients using machine learning in a real-world ICU cohort.

Acute-on-chronic liver failure is a complex condition with varied definitions, complicating risk stratification and targeted management. We apply unsu...

An artificial intelligence-powered learning health system to improve sepsis detection and quality of care: a before-and-after study.

Sepsis is a major global health crisis where early recognition and effective management remain significant challenges for healthcare systems. As part ...

Time-Series Machine Learning for Prediction of Bronchopulmonary Dysplasia.

OBJECTIVE: To build a time-series machine learning (ML) model that improves bronchopulmonary dysplasia (BPD) prediction compared with published online...

Quality and Reliability of AI Information on Dental Implant Failure: A Comparative Multi-Model Analysis.

OBJECTIVE: This study aimed to develop a consensus-based set of patient questions on dental implant failure and to compare the clarity, quality, accur...

Deep visual detection system for oral squamous cell carcinoma.

BACKGROUND: Oral Squamous Cell Carcinoma (OSCC) is a widespread and aggressive malignancy where early and accurate detection is essential for improvin...