Critical Care

MultiECGNet: A novel deep learning-based multi-format ensemble method for image-based electrocardiographic diagnosis of atrial fibrillation

To evaluate the performance of an ensemble classifier, MultiECGNet, using multi-format electrocardiographic (ECG) images for the diagnosis of atrial fibrillation (AF), and to compare its performance with a signal-based deep learning model. An ensemble of ECG classifiers was developed using four models derived by truncating the pre-trained EfficientNet B3 model at different feature extraction layer...

A Hybrid AutoML Ensemble Integrating Conventional Learners and Gradient-Boosting Models for Multi-Outcome Prediction in ICU Patients with Pseudomonas aeruginosa

Carbapenem resistance in Pseudomonas aeruginosa is increasing in intensive care units (ICUs). To enhance antimicrobial stewardship and infection control, we aimed to develop and validate a real-time interpretable hybrid Automated Machine Learning (AutoML) ensemble for multi-outcome prediction. We retrospectively analyzed 847 adult ICU admissions with P. aeruginosa isol ates at a tertiary hospital ...

Artificial Intelligence Enabled Phenogrouping of Heart Failure with Preserved Ejection Fraction Depicts Early and End-Stage Trajectories

Heart failure with preserved ejection fraction is challenging to diagnose, precluding the initiation of prognostic medications. A deeper understanding...

Leveraging Machine Learning for Developing and Validating a Neonatal Acute Kidney Injury Prediction Model (NEPHRO): A Comprehensive Evidence-Based Neonatal AKI Risk Stratification Tool

Acute kidney injury (AKI) is a serious and common complication among critically ill neonates. Preventing or treating AKI early requires timely predict...

CLIF-Net: Intersection-guided Cross-view Fusion Network for Infection Detection from Cranial Ultrasound

This paper addresses the problem of detecting possible serious bacterial infection (pSBI) of infancy, i.e. a clinical presentation consistent with bac...

Case-Control Matching Erodes Feature Discriminability for AI-driven Sepsis Prediction in ICUs: A Retrospective Cohort Study

Sepsis remains a leading cause of intensive care unit (ICU) mortality worldwide, and early detection is essential for improving survival through timel...

Development of a pilot machine learning model to predict successful cure in critically ill patients with community-acquired pneumonia

Severe community-acquired pneumonia (CAP) remains a major cause of critical illness, yet there are no validated early clinical criteria to predict sho...

Evaluating Few-Shot Prompting for Spectrogram-Based Lung Sound Classification Using a Multimodal Language Model

Traditional deep learning models for lung sound analysis require large, labeled datasets; multimodal LLMs may offer a flexible, prompt-based alternati...

CLINICAL VALIDATION OF SWAASA ARTIFICIAL INTELLIGENCE PLATFORM USING COUGH SOUNDS FOR SCREENING AND DIAGNOSIS OF RESPIRATORY DISEASES

Analysis of cough sounds have the potential to give a clue regarding the underlying respiratory disease. The Swaasa AI platform using artificial intel...

Machine learning models to detect opioid misuse in Emergency Department patients at triage

Emergency department (ED) encounters represent valuable opportunities to initiate evidence-based treatments for patients with opioid misuse, but few r...

Large Language Models Improve Coding Accuracy and Reimbursement in a Neonatal Intensive Care Unit

Diagnosis coding is essential for clinical care, research validity, and hospital reimbursement. In neonatal settings, manual coding is frequently erro...

White matter characterization in regions of edema surrounding meningioma brain tumor using diffusion MRI

White matter (WM) tract detection is critical in presurgical planning of tumor resection however, standard-of-care imaging techniques including T1-wei...

Multimodal Deep Learning for ARDS Detection

Poor outcomes in acute respiratory distress syndrome (ARDS) can be alleviated with tools that support early diagnosis. Current machine learning method...

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 approaches to patient care. The intensive care unit (ICU) ...

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 the urgent need for early detection to improve pati...

Evaluating Large Language Models for Automatic Detection of In-Hospital Cardiac Arrest: Multi-Site Analysis of Clinical Notes

In-hospital cardiac arrest (IHCA) affects over 200,000 patients annually in the United States, yet its detection through manual chart review remains r...

Spirometry parameter prediction using Acoustic characteristics of Cough

Spirometry evaluates lung function by measuring airflow post-maximal inspiration, using parameters like FEV1, FVC, and the FEV1/FVC ratio for respirat...

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-tuned with Low-Rank Adapters (LoRA) on ICU physician ...

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 developed an open-source ICU Capital Budgeting Wo...

Evaluating Large Language Model Diagnostic Performance on JAMA Clinical Challenges via a Multi-Agent Conversational Framework

Standard clinical LLM benchmarks use multiple-choice vignettes that present all information up front, unlike real encounters where clinicians iterativ...