Critical Care

A device-invariant multi-modal learning framework for respiratory disease classification.

Recent advances in cough sound analysis using deep learning techniques enable smartphone-based respiratory disease screening suitable for self-management care in a home setting, yet their utility is limited by device heterogeneity, population diversity, and challenges in multimodal integration. We propose a device-invariant, multimodal deep learning framework that jointly models cough acoustics, d...

A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data.

Computational blind challenges offer critical, unbiased opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the past decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral P...

Association between hypothermic machine perfusion parameters and graft function in deceased donor kidney transplantation.

BACKGROUND: Kidney transplantation (KT) is the most effective treatment for end-stage renal disease. Hypothermic machine perfusion (HMP) can improve r...

Balancing Innovation in Artificial Intelligence With Practical Sepsis Definitions.

Operationalizing Sepsis Definitions With Artificial Intelligence: Toward Smarter Antibiotic Use.

Application research on surveillance and predictive modeling of respiratory diseases in Baise City based on meteorological big data analysis.

Respiratory diseases are increasingly influenced by meteorological variability, yet few studies have applied high-resolution environmental data and ad...

Artificial intelligence in pediatric nephrology: current applications and emerging frameworks for evidence generation.

Artificial intelligence (AI) is rapidly transforming many areas of medicine. Pediatric nephrology may be a field of particular relevance due to its da...

Stratified Causal Inference for Intensive Care Unit Risk Prediction: Informatics-Based Modeling of Anesthetic Drug Combinations.

BACKGROUND: Postoperative intensive care unit (ICU) admission affects 15% to 20% of surgical patients and represents a major source of morbidity and h...

A machine learning model for prediction of early-onset neonatal sepsis in low-income and middle-income countries: development and validation study.

OBJECTIVE: Early-onset sepsis (EOS), which occurs within the first 72 hours of life, can often be fatal for neonates. Machine learning (ML) models dem...

Predictive machine learning algorithms for depression and anxiety disorders in six cancer types: a comprehensive multi-center population-based study.

OBJECTIVE: Machine learning (ML) has advanced predictive modeling in medical diagnosis and risk assessment through large clinical datasets, yet applic...

Explainable multimodal deep learning models for variable-length sequences in critically ill patients.

OBJECTIVE: Deep learning models have shown strong performance in predicting clinical events in critical care using structured electronic health record...

A Surface-Enhanced Raman Scattering-Digital Microfluidics Biosensing Platform Integrated with a 1D-Convolutional Neural Network for Noninvasive Detection of Inflammation Markers.

The noninvasive detection of multiple inflammatory markers (IMs) for early sepsis diagnosis is challenging. Therefore, herein, we innovatively develop...

Real-Time MRI With Deep Learning for Efficient Evaluation of Neuromuscular Breathing Impairment.

Efficient detection of breathing impairment is critical for treatment and prognosis in neuromuscular disorders. However, standard pulmonary function t...

Machine Learning Models for Mortality Prediction in Intensive Care Unit Patients With Ischemic Stroke Associated With Intracranial Artery Stenosis: Retrospective Cohort Study.

BACKGROUND: Mortality prediction in intensive care unit (ICU) patients with ischemic stroke complicated by intracranial artery stenosis or occlusion r...

Non-pharmacological treatment of AECOPD.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPDs) are acute events characterized by rapid worsening of dyspnea, cough, and sputum...

Predicting low oxygen in patients with acute COVID-19 infection isolating at home: a clinical prediction model.

RATIONALE: Identifying patients who could be safely managed at home versus those needing hospitalisation was a particular concern during early COVID-1...

Robust learning framework for a scalable remote monitoring of autonomic dysreflexia: use-case in spinal cord injury.

The autonomous nervous system (ANS) response in neurological disorders is a direct modifiable risk factor for cardiovascular health, however, difficul...

Natriuretic peptides and cardiac troponins in hypertension and heart failure: A contemporary review of the evidence.

Hypertension is the second leading cause of heart failure (HF), yet strategies for identifying hypertensive individuals at increased HF risk remain li...

Integration of single-cell and bulk transcriptomics with machine learning identifies LDHA as a lactate-related diagnostic and prognostic biomarker in sepsis.

Correction to: Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.