Intensivists

Optimizing sepsis mortality prediction using hybrid federated learning and explainable AI framework.

Machine Learning-Based Clusters of Vital Signs and Lactate Levels Predict Vasopressor Use in Sepsis.

OBJECTIVE: Sepsis remains a major clinical challenge because of its complex, heterogeneous, and multidimensional clustering patterns. This study aimed to investigate the association between vasopressor administration and machine learning-derived clusters based on initial vital signs and lactate measurements obtained in emergency department (ED) and intensive care unit (ICU) settings. METHODS: A re...

Development and deployment of an interpretable stacking ensemble model for predicting in-hospital mortality in ICU patients with chronic kidney disease and sepsis.

OBJECTIVE: To develop an interpretable stacking ensemble model for predicting in-hospital mortality in intensive care unit (ICU) patients with CKD and...

Machine learning to assess correlated sociodemographic and socioeconomic community-level data and association with sepsis incidence.

WRKY Transcription Factors: Integral Regulators of Defence Responses to Biotic Stress in Crops.

Crops are continually challenged by biotic stresses, including fungal, bacterial and viral pathogens and insect pests, which cause substantial yield a...

Evaluating a negotiation training program for family caregivers of older people using a Multiphase Optimization Strategy (MOST) design and protocol.

UNLABELLED: Traditional clinical trial designs such as the isolated two-arm randomized controlled trial (RCT) do not offer robust solutions for evalua...

Development of a new tool for prediction of hospital length of stay and intensive care needs in trauma patients using Machine Learning.

BACKGROUND: Trauma is a major global health burden leading to significant morbidity, disability, and mortality. Predictive models in trauma care tradi...

Multi-scale heart simulation augments the explainability of artificial intelligence-enabled electrocardiogram through provision of an electrocardiogram database labelled with cellular pathologies.

BACKGROUND AND OBJECTIVES: Although artificial-intelligence-enhanced electrocardiograms (AI-ECGs) offer prediction and diagnosis capabilities superior...

A multi-target therapeutic framework for Alzheimer's disease: an integrative mechanistic review.

BACKGROUND: Alzheimer's disease (AD) is increasingly recognized as a multifactorial network disorder in which amyloid and tau pathology interact with ...

Impact of continuing renin-angiotensin-aldosterone system inhibitors before surgery on intraoperative hypotensive events: a secondary analysis of the STOP-or-NOT Trial.

BACKGROUND: The multicentre STOP-or-NOT trial has shown that continuation of renin-angiotensin-aldosterone inhibitors (RAASis) before major noncardiac...

Association between the platelet to white blood cell ratio and short term mortality in critically ill patients with atherosclerotic cardiovascular disease: A retrospective study and machine learning with external validation.

BACKGROUND: The platelet to white blood cell ratio (PWR) has shown prognostic value in many diseases. Yet its predictive utility for patients with ath...

Accelerating genetic gain through integrated genomic selection in crop plants.

Meeting the projected 70% rise in agricultural output by 2050 to sustain a global population of 9.6 billion poses a formidable challenge amid intensif...

The role of AI-driven communication in delirium prevention, detection, and care for critically ill ICU patients: A systematic review with inductive thematic synthesis.

BACKGROUND: Delirium remains one of the most consequential complications among critically ill patients in ICUs, exerting profound effects on morbidity...

Using the Electronic Health Record and Artificial Intelligence to Guide Clinical Decision-Making.

Electronic medical records (EMR) have transformed how clinical information is documented, shared, and utilized over the past 60 years, and the additio...

Development and validation of interpretable machine learning models for dynamic prediction of prognosis in acute pancreatitis complicated by acute kidney injury: A multicenter study.

OBJECTIVE: This study aims to develop and validate interpretable machine learning (ML) models to dynamically predict mortality risk among intensive ca...

Shared inflammation in takotsubo syndrome and sepsis: Lipocalin-2 mediates neutrophil infiltration and cardiac damage.

BACKGROUND: Takotsubo syndrome (TTS) and sepsis often co-occur with poor outcomes, yet their underlying molecular mechanisms remain to be elucidated. ...

SERS on analyte-enriched blood for rapid, culture-free sepsis recognition and causative pathogen identification with super operational neural networks.

Sepsis remains a leading cause of morbidity and mortality, yet routine diagnostics are slow, culture-dependent, and often lack the sensitivity or spec...

Construction and validation of a machine learning-based risk prediction model for invasive mechanical ventilation in AECOPD patients complicated with respiratory failure.

OBJECTIVE: This study aimed to create and validate a machine learning (ML) model to predict the likelihood of invasive mechanical ventilation (IMV) in...

Integrating multi-omics and artificial intelligence fuels advanced target identification and drug discovery.

Target identification is pivotal for developing novel therapeutics in cancer and other diseases. Traditional experiment screening methods are constrai...

A New Proposed Approach to Categorize Sepsis Sub-phenotypes Based on Combined Trajectories of Perfusion Index and Central Venous Oxygen Saturation.

BACKGROUND: Circulatory disturbances in sepsis are heterogeneous but may manifest as a mismatch between peripheral perfusion and tissue oxygenation. P...