Critical Care

Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors.

The detection of small molecules is critical in many fields, but traditional electrochemical detection methods often exhibit limited accuracy. The construction of multi-mode sensors is a common strategy to improve detection accuracy. However, most existing multi-mode sensors rely on the separate analysis of each mode signal, which can easily lead to sensor failure when the deviation between differ...

Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning.

BACKGROUND: Sepsis is a life-threatening disease causing millions of deaths every year. It has been reported that programmed cell death (PCD) plays a critical role in the development and progression of sepsis, which has the potential to be a diagnosis and prognosis indicator for patient with sepsis.

Continuous non-contact monitoring of neonatal activity.

PURPOSE: Neonatal activity is an important physiological parameter in the neonatal intensive care unit (NICU). The degree of neonatal activity is asso...

Dissolved organic carbon estimation in lakes: Improving machine learning with data augmentation on fusion of multi-sensor remote sensing observations.

This paper presents a novel approach for estimating Dissolved Organic Carbon (DOC) concentrations in lakes considering both carbon sources and sink op...

Visit-to-visit blood pressure variability and clinical outcomes in peritoneal dialysis - based on machine learning algorithms.

This study aims to investigate the association between visit-to-visit blood pressure variability (VVV) in early stage of continuous ambulatory periton...

Convolutional long short-term memory neural network integrated with classifier in classifying type of asynchrony breathing in mechanically ventilated patients.

BACKGROUND AND OBJECTIVE: Asynchronous breathing (AB) occurs when a mechanically ventilated patient's breathing does not align with the mechanical ven...

Impact of Sepsis Onset Timing on All-Cause Mortality in Acute Pancreatitis: A Multicenter Retrospective Cohort Study.

BackgroundSepsis complicates acute pancreatitis (AP), increasing mortality risk. Few studies have examined how sepsis and its onset timing affect mort...

Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit.

OBJECTIVES: Developing and validating interpretable machine learning (ML) models for predicting whether triaged patients need to be admitted to the in...

An 8-point scale lung ultrasound scoring network fusing local detail and global features.

Manual lung ultrasound (LUS) scoring is influenced by clinicians' subjective interpretation, leading to potential inconsistencies and misdiagnoses due...

Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis.

OBJECTIVE: Blood pressure fluctuations during dialysis, including intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN), are common c...

Development and clinical evaluation of an AI-assisted respiratory state classification system for chest X-rays: A BMI-Specific approach.

PURPOSE: In this study, we aimed to develop and clinically evaluate an artificial intelligence (AI)-assisted support system for determining inhalation...

Hybrid multi-modality multi-task learning for forecasting progression trajectories in subjective cognitive decline.

While numerous studies strive to exploit the complementary potential of MRI and PET using learning-based methods, the effective fusion of the two moda...

Z-SSMNet: Zonal-aware Self-supervised Mesh Network for prostate cancer detection and diagnosis with Bi-parametric MRI.

Bi-parametric magnetic resonance imaging (bpMRI) has become a pivotal modality in the detection and diagnosis of clinically significant prostate cance...

An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.

Auscultation is a method that involves listening to sounds from the patient's body, mainly using a stethoscope, to diagnose diseases. The stethoscope ...

Perioperative goal-directed therapy with artificial intelligence to reduce the incidence of intraoperative hypotension and renal failure in patients undergoing lung surgery: A pilot study.

STUDY OBJECTIVE: The aim of this study was to investigate whether goal-directed treatment using artificial intelligence, compared to standard care, ca...

A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery.

BACKGROUND: Sepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonl...

Using a multi-strain infectious disease model with physical information neural networks to study the time dependence of SARS-CoV-2 variants of concern.

With the ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its increasing adaptation to humans, several variants o...

Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction.

Accurately labeling large datasets is important for biomedical machine learning yet challenging while modern data augmentation methods may generate no...

Tailoring ventilation and respiratory management in pediatric critical care: optimizing care with precision medicine.

PURPOSE OF REVIEW: Critically ill children admitted to the intensive care unit frequently need respiratory care to support the lung function. Mechanic...

A multi-classification deep neural network for cancer type identification from high-dimension, small-sample and imbalanced gene microarray data.

Gene microarray technology provides an efficient way to diagnose cancer. However, microarray gene expression data face the challenges of high-dimensio...