Intensivists

External Validation of the PIPRA Model for Postoperative Delirium Prediction in a Japanese ICU Cohort: Integrating AI-Based Risk Assessment into Perioperative Care.

BACKGROUND: Postoperative delirium (POD) is a prevalent and serious complication in older surgical patients, linked to prolonged hospitalization, higher morbidity, and long-term cognitive decline. Artificial intelligence-based prediction models have advanced perioperative risk assessment, yet the utility of the Pre-Interventional Predictive Risk Assessment (PIPRA) model in critically ill populatio...

Comparing ChatGPT-4o and Gemini 1.5 Pro in Adolescent Psychiatric Emergencies: A Real-World Evaluation of AI Support in Suicide Risk Assessment.

ObjectiveThis study aimed to evaluate the performance of large language models-ChatGPT-4o and Gemini 1.5 Pro-in assessing suicide risk and guiding treatment in adolescents presenting to the emergency department with suicidal ideation and/or attempts.Materials and MethodsA retrospective review was conducted on child psychiatry consultation notes from 36 adolescents evaluated between February and Ma...

Resolving the multiscale design trade-off in bone tissue engineering: From generative design to digital twins.

The clinical translation of bone tissue engineering is constrained by a severe multiscale design trade-off: the requirement for load-bearing mechanica...

Automated estimation of drain output in postoperative patients using deep learning on clinical images.

Postoperative drains are essential components of care in general surgery and intensive care units, where accurate monitoring of drain output is critic...

Development and validation of a machine learning model for predicting positive blood cultures using vital signs in ICU patients.

Bloodstream infections require timely and appropriate diagnosis and treatment, as inadequate management is associated with higher mortality. Previous ...

Association between platelet-albumin-bilirubin score and 30-day in-hospital mortality in patients with sepsis: evidence from a large database and machine learning modeling.

Emerging technologies and AI-assisted tools in cardiopulmonary monitoring.

PURPOSE OF REVIEW: Cardiopulmonary monitoring is fundamental to critical care, yet traditional approaches rely on simplified thresholds that capture o...

Lightweight large language models for early sepsis prediction via a semantic abstraction rule engine.

Sepsis remains a major global health challenge due to its high mortality rate and the difficulty of predicting its onset from nonspecific early sympto...

LSTM-GRU hybrid model for multi-layer microclimate prediction in solar greenhouse.

Precise microclimate control across vertical canopy layers is critical for optimizing crop production in horticulture solar greenhouses, yet existing ...

Explainable machine learning using urinary metabolomics to predict pediatric sepsis-associated acute kidney injury: a two-center prospective observational study.

Sepsis-induced acute kidney injury (S-AKI) is a common and serious complication in critically ill children with a poor prognosis, and its early and ac...

Machine learning-based precision subtyping and risk prediction in sepsis: a retrospective analysis using MIMIC-IV database.

Enhancing large language model clinical support information with machine learning risk and explainability: a feasibility study.

BACKGROUND: Current machine learning (ML) prediction models offer limited guidance for individualized actionable management. Large language models (LL...

Risk prediction models for incident heart failure: a systematic review and meta-analysis.

BACKGROUND: Early identification of patients at risk of heart failure (HF) provides opportunities for preventative management. Though models have been...

The role of triglyceride-glucose (TyG) index in predicting mortality risk in critically ill patients with sepsis: a machine learning model in a cohort study.

Development of a calibrated logistic regression clinical risk score for predicting postoperative sepsis following retrograde intrarenal surgery.

Predicting Postoperative Cognitive Dysfunction in Older Cardiac Surgery Patients: An Integrated Machine Learning Approach With a Visual Nomogram.

OBJECTIVES: To integrate machine learning algorithms to identify key risk factors for postoperative cognitive dysfunction (POCD) in older cardiac surg...

Development of a real-world, therapeutic drug monitoring-informed model to predict teicoplanin daily dose in pediatric intensive care unit patients with bacterial infections.

INTRODUCTION: Teicoplanin is commonly used to treat Gram-positive bacterial infections in the intensive care unit (ICU). However, evidence to support ...

Transformer-based multi-agent traffic simulation for autonomous vehicle testing in shared urban road segments.

Microscopic traffic simulation plays a crucial role in the development and testing of autonomous driving systems. However, accurately reproducing traf...

Targeted Surface-Enhanced Raman Scattering for Highly Accurate Identification of Bacterial Species and Finding Spectral Signatures with Explainable Artificial Intelligence.

The analysis of complex Raman spectra from biological samples has traditionally relied on conventional chemometric-based methods, the performance of w...

Early detection of heart failure using trend similarity metrics combined with hybrid deep learning based on real-world health monitoring data.

Heart failure (HF) is a global health crisis, with over 18 million annual deaths attributed to cardiovascular diseases. Early detection is critical to...