Critical Care

Interpretable Multi-Branch Architecture for Spatiotemporal Neural Networks and Its Application in Seizure Prediction.

Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signals have emerged as promising tools for seizure prediction (SP), which explore the spatiotemporal biomarkers in an epileptic brain. Generally, these CNNs capture spatiotemporal features at single spectral resolution. However, epileptiform EEG signals contain irregular neural oscillations of different ...

An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia.

BACKGROUND: Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significantly associated with poor outcomes. However, there is currently a lack of reliable and interpretable tools for assessing the risk of in-hospital mortality in VAP patients. This study aims to develop an interpretable machine learning (ML) prediction model to enhance the assessment of in-hospital mort...

Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.

Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learnin...

Interpretable machine learning for predicting sepsis risk in emergency triage patients.

The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods...

External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis.

BACKGROUND: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems...

Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.

Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the po...

Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients.

BACKGROUND: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to pr...

Predicting the risk of gastroparesis in critically ill patients after CME using an interpretable machine learning algorithm - a 10-year multicenter retrospective study.

BACKGROUND: Gastroparesis following complete mesocolic excision (CME) can precipitate a cascade of severe complications, which may significantly hinde...

Retrospective analysis of amantadine response and predictive factors in intensive care unit patients with non-traumatic disorders of consciousness.

BACKGROUND: Disorders of consciousness (DoC) in non-traumatic ICU-patients are often treated with amantadine, although evidence supporting its efficac...

A discriminative multi-modal adaptation neural network model for video action recognition.

Research on video-based understanding and learning has attracted widespread interest and has been adopted in various real applications, such as e-heal...

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

BACKGROUND: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including admini...

Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology.

OBJECTIVE: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer....

OTMorph: Unsupervised Multi-Domain Abdominal Medical Image Registration Using Neural Optimal Transport.

Deformable image registration is one of the essential processes in analyzing medical images. In particular, when diagnosing abdominal diseases such as...

Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study.

BACKGROUND: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving o...

Characteristics and outcomes of pulmonary barotrauma in patients with COVID-19 ARDS: A retrospective observational study.

INTRODUCTION: Pulmonary barotrauma in coronavirus disease-2019 (COVID-19) acute respiratory distress syndrome (ARDS) carries high risk of mortality. W...

Quantification of L-lactic acid in human plasma samples using Ni-based electrodes and machine learning approach.

This work presents a robust strategy for quantifying overlapping electrochemical signatures originating from complex mixtures and real human plasma sa...

Multi-scale multi-object semi-supervised consistency learning for ultrasound image segmentation.

Manual annotation of ultrasound images relies on expert knowledge and requires significant time and financial resources. Semi-supervised learning (SSL...

Machine learning-based forecast of Helmet-CPAP therapy failure in Acute Respiratory Distress Syndrome patients.

BACKGROUND AND OBJECTIVE: Helmet-Continuous Positive Airway Pressure (H-CPAP) is a non-invasive respiratory support that is used for the treatment of ...

An artificial intelligence application to predict prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma: an establishment and validation study.

BACKGROUND: Prolonged dependence on mechanical ventilation is a common occurrence in clinical ICU patients and presents significant challenges for pat...

Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches.

Sepsis-associated encephalopathy (SAE) is common in septic patients, characterized by acute and long-term cognitive impairment, and is associated with...