Critical Care

A Machine Learning-Based Model for Individualized Prediction of Vancomycin Concentration-Time Curves in ICU Patients.

Therapeutic drug monitoring is essential for ensuring the efficacy and safety of vancomycin therapy in critically ill patients. This study aimed to develop a machine learning model for individualized prediction of vancomycin concentration-time curves in ICU patients. Adult ICU patients who received intravenous vancomycin and underwent therapeutic drug monitoring at Peking Union Medical College Hos...

DILIConsult: A Multi-Agent Large Language Model Framework for Evaluating Drug-Induced Liver Injury in ICU Settings.

BACKGROUND: Large language models (LLMs) can support clinical decision-making by parsing databases and extracting relevant information. However, evaluating drug-induced liver injury (DILI) often requires processing lengthy clinical histories alongside reference materials like LiverTox, which can exceed context lengths of conventional LLMs. Challenges such as information truncation hinder standard ...

Predicting ventilation from single breathing phase non-contrast CT using Swin Transformers.

BACKGROUND: Pulmonary ventilation imaging enables functional avoidance radiotherapy treatment plans by quantifying regional lung function. However, cu...

Dynamic machine learning prediction of persistent AKI after cardiac surgery with modifiable perioperative risk factors.

Early identification and prevention of persistent acute kidney injury (pAKI) remain challenging due to delayed biochemical markers and limited tools t...

Automating the segmentation, date extraction, and classification of multi-report PDFs in outside medical records using optical character recognition and generative artificial intelligence.

OBJECTIVES: Patients referred for specialized care often arrive with outside medical records (OMRs) compiled into multi-report PDFs that include imagi...

Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.

BACKGROUND: Critically ill patients generate large volumes of complex data, creating challenges for timely clinical decision making in intensive care ...

Artificial intelligence in extracorporeal technology: Current applications and future directions-A narrative review.

Artificial intelligence (AI) has rapidly emerged as a transformative force across multiple scientific and clinical domains, demonstrating remarkable c...

A Systematic Review and Meta-analysis of Externally Validated Epic Clinical Decision Support Tools.

BACKGROUND: Clinical Decision Support (CDS) tools integrated with Electronic Health Records increasingly guide clinical practice. Epic Systems, storin...

Conventional and novel platelet parameters: Clinical and laboratory approaches.

INTRODUCTION: Platelet count and related indices obtained from the complete blood count (CBC) play an important role in the diagnosis, monitoring, and...

Automated hemodynamic management system for the prevention of hypotension and cardiac output reduction during general anesthesia: preclinical experimental validation.

PURPOSE: Intraoperative hypotension and cardiac output (CO) reduction adversely affect outcomes and require cause-specific treatment against vasodilat...

AI-guided multi-omics analysis identifies NPC1-modulated susceptibility to SARS-CoV-2 infection under PM2.5 exposure.

Exposure to airborne fine particulate matter (PM2.5) has been linked to increased risk of the severe acute respiratory syndrome coronavirus 2 (SARS-Co...

Using machine learning algorithms to predict MACE in peritoneal dialysis patients.

The study aimed to predict the risks of Major adverse cardiac events (MACE) in patients undergoing peritoneal dialysis (PD) with machine learning (ML)...

Use of Artificial Intelligence to Predict Outcomes in Critically Ill Patients Receiving Extracorporeal Membrane Oxygenation: A Systematic Review.

Extracorporeal membrane oxygenation (ECMO) is widely used in patients with severe cardiac or respiratory failure. ECMO is resource and cost intensive ...

Practical Integration of Bedside Real-Time Lung Ultrasound and Electrical Impedance Tomography for Guiding Personalized Perioperative Lung Protective Ventilation in Cardiothoracic and Vascular Anesthesia.

Patients undergoing cardiothoracic and vascular surgery are at uniquely high risk for postoperative pulmonary complications due to the confluence of s...

Extraction of distant recurrence sites for breast cancer patients from free-text clinical notes using large language models.

OBJECTIVES: Accurate documentation of distant recurrence sites in breast cancer is essential for evaluating treatment effectiveness and outcomes resea...

Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence.

BACKGROUND: Delayed or missed diagnosis of congenital heart disease (CHD) contributes to excess pediatric mortality worldwide. Echocardiography (echo)...

Establishment and validation of a machine learning-based prediction model for sepsis-induced coagulopathy.

A study of large language models for patient information extraction: Model architecture, fine-tuning strategy, and multi-task instruction tuning.

BACKGROUND: Natural language processing (NLP) is a key technology to extract patient information from clinical narratives to support healthcare applic...

Optimal machine learning models and factors for global infectious diseases: An ecological analysis.

BACKGROUND: Infectious diseases are a major global public health challenge. Socioeconomic and environmental changes have complicated the situation, un...

Water quality prediction based on multi-model ensemble learning in a large-scale basin: A case study of the Poyang Lake Basin, China.

Total phosphorus (TP) poses a severe threat to the health of fluvial and lacustrine ecosystems in China. Accurate prediction of TP and analysis of its...