Critical Care

MVT-Net: A novel cervical tumour segmentation using multi-view feature transfer learning.

Cervical cancer is one of the most aggressive malignant tumours of the reproductive system, posing a significant global threat to women's health. Accurately segmenting cervical tumours in MR images remains a challenging task due to the complex characteristics of tumours and the limitations of traditional methods. To address these challenges, this study proposes a novel cervical tumour segmentation...

Physiological Response of Tissue-Engineered Vascular Grafts to Vasoactive Agents in an Ovine Model.

Tissue-engineered vascular grafts (TEVGs) are emerging as promising alternatives to synthetic grafts, particularly in pediatric cardiovascular surgery. While TEVGs have demonstrated growth potential, compliance, and resistance to calcification, their functional integration into the circulation, especially their ability to respond to physiological stimuli, remains underexplored. Vasoreactivity, the...

Enhancing early mortality prediction for sepsis-associated acute respiratory distress syndrome patients via optimized machine learning algorithm: development and multiple databases' validation of the SAFE-Mo.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is associated with high mortality, with sepsis accounts for 31-34% of cases. Given the global b...

Validation of an artificial intelligence-based algorithm for predictive performance and risk stratification of sepsis using real-world data from hospitalised patients: a prospective observational study.

OBJECTIVE: The heterogeneous nature of sepsis renders determining its underlying causes difficult, which may delay diagnosis and intervention. VitalCa...

Disease entity impacts muscle wasting in the ICU with COVID-19 patients losing muscle nearly twice as fast.

Muscle loss in critically ill patients, particularly during prolonged ICU stays, poses significant challenges to recovery and long-term outcomes. ICU-...

Prediction of Sepsis after Endourologic Kidney Stone Surgery: A Machine Learning Approach.

Sepsis secondary to urinary tract infection after kidney stone surgery is associated with considerable morbidity. Limited research examines the use o...

From PDMS-based exposure profiling to machine learning-predicted serum concentrations: SVOC exposure disparities across occupational and environmental scenarios.

Occupational exposure to semi-volatile organic compounds (SVOCs) among various population groups has garnered insufficient attention. We investigated ...

Development of a Machine Learning-Based Predictive Model for Postoperative Delirium in Older Adult Intensive Care Unit Patients: Retrospective Study.

BACKGROUND: Delirium is a prevalent phenomenon among patients admitted to the geriatric intensive care unit (ICU) and can adversely impact prognosis a...

Can we predict the future of respiratory failure prediction?

BACKGROUND: Mortality in patients with acute respiratory failure remains high. Predicting progression of acute respiratory failure may be critical to ...

Prediction of Acute Kidney Injury in Critically ill Patients with Community-Acquired Pneumonia Using Machine Learning.

BackgroundThe incidence of acute kidney injury (AKI) is increased in patients with community-acquired pneumonia (CAP), contributing to poor outcomes i...

Traumatic brain injury management in the intensive care unit: standard of care and knowledge gaps.

Despite advances in the management of traumatic brain injury (TBI) in the intensive care unit (ICU), and the implementation of new diagnostic techniqu...

Precision Medicine and Machine Learning to predict critical disease and death due to Coronavirus disease 2019 (COVID-19).

INTRODUCTION: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes Coronavirus Disease 2019 (COVID-19) and induces activation of in...

Harnessing AI in critical care: opportunities, challenges and key steps for success.

BACKGROUND: The integration of artificial intelligence (AI) into critical care offers significant potential to enhance early diagnosis, predict patien...

Chemokine-like factor-like MARVEL transmembrane domain-containing 1 identified as a novel target protein for immune dysregulation in sepsis: A machine learning and molecular dynamics framework for diagnostic biomarkers and therapeutic exploration.

BACKGROUND: Sepsis, a systemic inflammatory response syndrome precipitated by infection, involves critical immune dysregulation. Here, we aimed to eva...

Empirical mode decomposition in clinical signal analysis: A systematic review.

This systematic review examines the transformative applications of empirical mode decomposition (EMD) in healthcare, focusing on its ability to analys...

Exploring the molecular mechanisms of lactylation-related biological functions and immune regulation in sepsis-associated acute kidney injury.

Lactylation, a novel post-translational modification, has been implicated in various pathophysiological processes; however, its role in sepsis-associa...

Clinical Utility of Pulmonary Function Testing in Assessing Longitudinal Outcomes of Deployed Veterans with Preserved Spirometry.

Deployment to the Southwest Asia Theater of Military Operations is associated with new-onset respiratory symptoms, yet commonly-used parameters on pul...

Interpretable Machine Learning Prediction Model for Predicting Mortality Risk of ICU Patients With Pressure Ulcers Based on the Braden Scale: A Clinical Study Based on MIMIC-IV.

AIMS: This study was to create an interpretable machine learning model to predict the risk of mortality within 90 days for ICU patients suffering from...

Development and validation of a cardiac surgery-associated acute kidney injury prediction model using the MIMIC-IV database.

OBJECTIVE: This study aimed to develop an innovative early prediction model for acute kidney injury (AKI) following cardiac surgery in intensive care ...

Multivariate multi-horizon time-series forecasting for real-time patient monitoring based on cascaded fine tuning of attention-based models.

The real-time forecasting of critical physiological indicators in intensive care units (ICUs) is essential for early intervention and clinical decisio...