Critical Care

EMBANet: A flexible efficient multi-branch attention network.

Recent advances in the design of convolutional neural networks have shown that performance can be enhanced by improving the ability to represent multi-scale features. However, most existing methods either focus on designing more sophisticated attention modules, which leads to higher computational costs, or fail to effectively establish long-range channel dependencies, or neglect the extraction and...

Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making.

UNLABELLED: The escalating therapeutic use of methadone has coincided with an increase in accidental ingestions, particularly among children ≤ 5 years. This study utilized machine learning (ML) methodologies on data from the National Poison Data System (NPDS) to predict pediatric methadone poisoning outcomes to enhance clinical decision-making. We analyzed 140 medical parameters from pediatric pat...

Supervised Contrastive Learning Framework and Hardware Implementation of Learned ResNet for Real-Time Respiratory Sound Classification.

This paper presents a supervised contrastive learning (SCL) framework for respiratory sound classification and the hardware implementation of learned ...

Web server-based deep learning-driven predictive models for respiratory toxicity of environmental chemicals: Mechanistic insights and interpretability.

Respiratory toxicity of chemicals is a common clinical and environmental health concern. Currently, most in silico prediction models for chemical resp...

Joint Energy-Based Model for Semi-Supervised Respiratory Sound Classification: A Method of Insensitive to Distribution Mismatch.

Semi-supervised learning effectively mitigates the lack of labeled data by introducing extensive unlabeled data. Despite achieving success in respirat...

Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.

The healthcare industry is generating a massive volume of data, promising a potential goldmine of information that can be extracted through machine le...

Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms.

Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate intervention due to neuropsychological issues. Howeve...

Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study.

OBJECTIVE: The aim of this study was to investigate the correlation between age, red cell distribution width (RDW) levels, and 180-day and 1-year mort...

Characterizing drivers of change in intraoperative cerebral saturation using supervised machine learning.

Regional cerebral oxygen saturation (rSO) is used to monitor cerebral perfusion with emerging evidence that optimization of rSO may improve neurologic...

Application of artificial intelligence and machine learning in bovine respiratory disease prevention, diagnosis, and classification.

Bovine respiratory disease (BRD) is the leading infectious disease in cattle, resulting in significant economic losses and welfare concerns in beef an...

Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study.

BACKGROUND: Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availabi...

Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.

OBJECTIVES: To describe the deployment of pediatric Calculated Assessment of Risk and Triage (pCART), a machine learning (ML) model to predict the ris...

Artificial intelligence-based cardiac transthyretin amyloidosis detection and scoring in scintigraphy imaging: multi-tracer, multi-scanner, and multi-center development and evaluation study.

INTRODUCTION: Providing tools for comprehensively evaluating scintigraphy images could enhance transthyretin amyloid cardiomyopathy (ATTR-CM) diagnosi...

Neural mechanisms, influencing factors and interventions in empathic pain.

Empathic pain, defined as the emotional resonance with the suffering of others, is akin to the observer's own experience of pain and is vital for buil...

Multimodal convolutional neural networks for the prediction of acute kidney injury in the intensive care.

Increased monitoring of health-related data for ICU patients holds great potential for the early prediction of medical outcomes. Research on whether t...

Development and usability evaluation of a nurse-led clinical decision support system (AI-AntiDelirium) for management of intensive care unit delirium: A mixed methods study.

BACKGROUND: Clinical decision support systems (CDSS) have been identified to aid clinical decision-making, but few studies focus on the application of...

Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the forme...

Weakly supervised multi-modal contrastive learning framework for predicting the HER2 scores in breast cancer.

Human epidermal growth factor receptor 2 (HER2) is an important biomarker for prognosis and prediction of treatment response in breast cancer (BC). HE...

Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak.

BACKGROUND: Controlling re-emerging outbreaks such as COVID-19 is a critical concern to global health. Disease forecasting solutions are extremely ben...

Machine Learning Tool for New Selective Serotonin and Serotonin-Norepinephrine Reuptake Inhibitors.

Depression, a serious mood disorder, affects about 5% of the population. Currently, there are two groups of antidepressants that are the first-line tr...