Hospital-Based Medicine

Intensivists

Latest AI and machine learning research in intensivists for healthcare professionals.

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Multimodal Deep Learning for ARDS Detection

Poor outcomes in acute respiratory distress syndrome (ARDS) can be alleviated with tools that suppor...

Detecting Stigmatizing Language in Clinical Notes with Large Language Models for Addiction Care

Recent studies have found that stigmatizing terms can incline physicians to pursue punitive approach...

Evaluating Deep Learning Sepsis Prediction Models in ICUs Under Distribution Shift: A Multi-Centre Retrospective Cohort Study

Sepsis remains a leading cause of mortality in intensive care units (ICUs) worldwide, underscoring t...

Toward Digital Twins in the Intensive Care Unit: A Medication Management Case Study

To evaluate the efficacy of digital twins developed using a large language model (LLaMA-3), fine-tun...

Intensive Care Unit Capital-Budgeting Workbench: An Open-Source Decision-Support Application for High-Acuity Investment Planning

Capital budgeting in intensive care units (ICUs) demands rapid, high-stakes investment decisions. We...

Developing an ICU Mortality Risk Prediction Model for Acute Myocardial Infarction Patients Based on Machine Learning

Patients with acute myocardial infarction (AMI) are in a critical condition, facing a high risk of d...

CardiacGPT™: A Real-Time AI Assistant for Intraoperative Guidance and Postoperative Decision Support in Cardiac Surgery

Cardiac surgery is one of the most complex and high-stakes areas of medicine, where intraoperative d...

Scalable screening for emergency department missed opportunities for diagnosis using sequential eTriggers and large language models

Missed opportunities for diagnosis (MODs), sometimes termed diagnostic errors, are a major cause of ...

Personalized Hemodynamic Management Using Reinforcement Learning to Prevent Persistent Acute Kidney Injury After Cardiac Surgery

Acute kidney injury (AKI) affects one-third of patients after cardiac surgery and increases morbidit...

Multi-View Echocardiographic Embedding for Accessible AI Development

Echocardiography serves as a cornerstone of cardiovascular diagnostics through multiple standardized...

Personalized Fluid Management in Patients with Sepsis and AKI: A Casual Machine Learning Approach

Intravenous (IV) fluids are cornerstone for management of acute kidney injury (AKI) after sepsis but...

Development and Validation of a parsimonious AI-Based Risk Score for Mortality in Heart Failure: A UK cohort study

Accurate risk stratification in heart failure (HF) is crucial to guide clinical decisions, optimise ...

Dynamic Lymphocyte Recovery Patterns Predict 90-Day Mortality in Sepsis: A Machine Learning-Enhanced Analysis of the MIMIC-IV Cohort

Sepsis-induced immunosuppression, characterized by lymphopenia, is associated with adverse outcomes....

Development of a Multi-Model Ensemble Tool for Early Prediction of 48-Hour Respiratory Failure Risk in CAP Patients

To develop a predictive tool capable of early identification of the risk of acute respiratory failur...

Predicting Carbapenem Resistance in Hospitalized Patients Using Machine Learning: A Retrospective Analysis of the MIMIC-III Database

Carbapenem-resistant Gram-negative bacteria (CR-GNB) represent a major health challenge due to limit...

Analyzing Information Disparities across Modalities in Mortality Prediction

Recent advances in deep learning have enabled the integration of heterogeneous data modalities for c...

Continuous Multimodal AI with Wearable Vital Signs Predicts Postoperative Complications in the General Ward

Surgery is inherently associated with complications, making early detection the cornerstone of timel...

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