Critical Care

Comparison of Time- and Frequency-Domain Methods for Assessing Brain Compliance in Brain-Injured Patients Monitored With Intraparenchymal Intracranial Pressure Sensors

Intracranial pressure (ICP) monitoring is commonly used in neuro-intensive care, but its utility may be limited by a suboptimal use. The brain pressure-volume relationship, a potential predictor of neurological health, is now approached using time-domain methods, which can be challenging to implement. Frequency-domain methods may offer an alternative, but their relationship with time-domain metric...

Physician gestalt compared with AI model to predict intubation in critically ill patients

Intubation and mechanical ventilation are associated with high mortality. Accurately predicting which patients are at the highest risk of intubation can enable interventions to reduce their risk. The performance of intensive care physicians to predict the need for intubation within the next 24 hours for medically critically ill patients is unknown. Machine learning models are adept at prediction t...

Predicting Disease Progression in Critically Ill Patients Using Frequency-Enhanced Time-series Forecasting

Accurate disease progression prediction is vital for managing critically ill patients in intensive care. Existing deep learning approaches mainly oper...

Artificial intelligence for aortic valve calcium score quantification by echocardiography

Aortic valve calcification (AVC), as measured by gold-standard computed tomography (CT) Agatston score, provides an anatomic assessment of aortic sten...

Multitask Artificial Intelligence–Based Electrocardiogram Tool for Preoperative Cardiac Testing in Noncardiac Surgery: Retrospective Cohort Study of Health Care Utilization and Costs

Preoperative cardiovascular (CV) risk stratification is essential in non-cardiac surgery, but conventional testing is frequently overused, increasing ...

Machine Learning Prediction of Blood Pressure Control in Patients With Hypertension and Heart Failure Using Longitudinal Clinical Data

To develop and validate machine learning models for predicting Blood Pressure (BP) control status using demographic characteristics and longitudinal B...

DeepCRI: Real-time EEG-based Prognostication after Cardiac Arrest

Accurate prediction of neurological outcome after cardiac arrest is essential for guiding intensive care decisions. Electroencephalography (EEG) suppo...

Hypergraph-Based Doubly Robust Estimation for Causal Inference of Drug Combination Effects in Heart Failure Treatment

Disease management for heart failure with preserved ejection fraction (HFpEF) requires understanding the comparative effectiveness of real-world drug ...

Comparison of local large language models for extraction of signs and symptoms data from electronic health records

Electronic health records (EHRs) provide a large source of data that can be used for research purposes. Extraction of information from unstructured cl...

Detecting Mental Disorders in Social Media Using a Transformer-Based Ensemble of Binary Classifiers

This study introduces a novel transformer-based ensemble framework for the multi-label detection of mental health disorders from social media posts. U...

Non-temporal tree-based models outperform temporal deep learning models in the prediction of chemotherapy-induced side effects from longitudinal laboratory data

The increasing availability of electronic health records (EHRs) provides opportunities to apply machine learning (ML) methods in support of clinical d...

Automated Sleep Stage and Event Detection Algorithms Using Quality-Controlled PSG Annotations

To develop machine-learning models for sleep stage classification, arousal detection, and respiratory event detection from polysomnography (PSG), and ...

Understanding Uncertainty in Large Language Model Predictions of Early Death in Critically Ill Patients: A Conformal Prediction Approach

Early prediction of in-hospital death remains a significant challenge due to the limited availability of structured data during initial admission. Uns...

Communication Efficient Cooperative Edge AI via Event-Triggered Computation Offloading

Rare events, despite their infrequency, often carry critical information and require immediate attentions in mission-critical applications such as a...

Deployable machine learning-based decision support system for tracheostomy in acute burn patients.

BACKGROUND: Airway obstruction is a common emergency in acute burns with high mortality. Tracheostomy is the most effective method to keep patency of ...

Association of early enoxaparin prophylactic anticoagulation with ICU mortality in critically ill patients with chronic obstructive pulmonary disease: a machine learning-based retrospective cohort study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major contributor to global morbidity and mortality, particularly during acute exacerbat...

Interpretable AI-driven multi-objective risk prediction in heart failure patients with thyroid dysfunction.

INTRODUCTION: Heart Failure (HF) complicated by thyroid dysfunction presents a complex clinical challenge, demanding more advanced risk stratification...

Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning.

OBJECTIVE: The purpose of this study was to develop and validate machine learning models that can predict superaverage length of stay in hypercapnic-t...

Predicting in-hospital mortality in patients with alcoholic cirrhosis complicated by severe acute kidney injury: development and validation of an explainable machine learning model.

BACKGROUND: At present, there are no specialized models for predicting mortality risk in patients with alcoholic cirrhosis complicated by severe acute...

Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach.

BACKGROUND: Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanis...