Critical Care

Fluorescence excitation-emission matrix spectroscopy combined with machine learning for the classification of viruses for respiratory infections.

Significant efforts were currently being made worldwide to develop a tool capable of distinguishing between various harmful viruses through simple analysis. In this study, we utilized fluorescence excitation-emission matrix (EEM) spectroscopy as a rapid and specific tool with high sensitivity, employing a straightforward methodological approach to identify spectral differences between samples of r...

The Hemodynamic Management and Postoperative Outcomes After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy: A Prospective Observational Study.

Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) has become standard treatment for peritoneal cancers and metastases, significantly enhancing survival rates. This study evaluated the relationship between tumor burden, hemodynamic management, and postoperative outcomes after CRS-HIPEC. This study included 203 patients undergoing CRS-HIPEC. The study was registered ...

Hope for the best prepare for the worst: acute kidney disease and catastrophic comorbidities (a case report).

It is evident that Acute Kidney Injury (AKI) is an independent risk factor for both the survival of patients and their kidneys. Here, we present a cas...

Noninvasive estimation of PaCO from volumetric capnography in animals with injured lungs: an Artificial Intelligence approach.

To investigate the feasibility of non-invasively estimating the arterial partial pressure of carbon dioxide (PaCO) using a computational Adaptive Neur...

Comparison of population pharmacokinetic modeling and machine learning approaches for predicting voriconazole trough concentrations in critically ill patients.

BACKGROUND: Despite the widespread use of voriconazole in antifungal treatment, its high pharmacokinetic and pharmacodynamic variability may lead to s...

The predictive value of heparin-binding protein for bacterial infections in patients with severe polytrauma.

INTRODUCTION: Heparin-binding protein is an inflammatory factor with predictive value for sepsis and participates in the inflammatory response through...

Impact of a trace mineral injection at weaning on growth, behavior, and inflammatory, antioxidant, and immune responses of beef calves.

Two experiments evaluated the effects of an injectable trace mineral (ITM) solution at weaning on trace mineral (TM) status, inflammatory and antioxid...

Machine Learning Model for Risk Prediction of Prolonged Intensive Care Unit in Patients Receiving Intra-aortic Balloon Pump Therapy during Coronary Artery Bypass Graft Surgery.

This study aimed to construct machine learning models and predict prolonged intensive care units (ICU) stay in patients receiving perioperative intra-...

Machine learning for predicting acute myocardial infarction in patients with sepsis.

Acute myocardial infarction (AMI) and sepsis are the leading causes of high mortality rates in intensive care units. While sepsis frequently affects t...

Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.

The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic...

Artificial intelligence in respiratory care.

The evolution of artificial intelligence (AI) has revolutionised numerous aspects of our daily lives, with profound implications across various sector...

Machine learning analysis of CD4+ T cell gene expression in diverse diseases: insights from cancer, metabolic, respiratory, and digestive disorders.

CD4 T cells play a pivotal role in the immune system, particularly in adaptive immunity, by orchestrating and enhancing immune responses. CD4 T cell-r...

Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis.

BACKGROUND: Predicting mortality in sepsis-related acute kidney injury facilitates early data-driven treatment decisions. Machine learning is predicti...

Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial.

INTRODUCTION: As surgical accessibility improves, the incidence of postoperative complications is expected to rise. The implementation of a precise an...

Predictive modeling of ICU-AW inflammatory factors based on machine learning.

BACKGROUND: ICU-acquired weakness (ICU-AW) is a common complication among ICU patients. We used machine learning techniques to construct an ICU-AW inf...

Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.

Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in c...

Recognizing SARS-CoV-2 infection of nasopharyngeal tissue at the single-cell level by machine learning method.

SARS-CoV-2 has posed serious global health challenges not only because of the high degree of virus transmissibility but also due to its severe effects...

Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.

BACKGROUND: Blood transfusion (BT) is a critical aspect of medical care for surgical patients in the Intensive Care Unit (ICU). Timely and accurate id...

Exploring the prognostic impact of triglyceride-glucose index in critically ill patients with first-ever stroke: insights from traditional methods and machine learning-based mortality prediction.

BACKGROUND: The incidence and mortality of first-ever strokes have risen sharply, especially in the intensive care unit (ICU). Emerging surrogate for ...

A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes.

Nocturnal hypoglycemia is a critical problem faced by diabetic patients. Failure to intervene in time can be dangerous for patients. The existing earl...