Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1378/chest.100.2.590-a | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Significance of Relative Left Ventricle Wall Thickness in Predicting Acute Kidney Injury After Video-Assisted Thoracoscopic Surgery for Lung Cancer.
Br J Hosp Med (Lond)
December 2024
Department of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
Given the strong association between relative wall thickness (RWT) and cardiovascular dysfunction, this study aims to explore RWT as a novel cardiovascular indicator to predict the risk of acute kidney injury (AKI) after lung cancer surgery and guide clinical interventions. This study retrospectively analyzed 170 patients who underwent video-assisted thoracoscopic surgery (VATS) for lung cancer in Nanjing First Hospital, China, between January 2022 and December 2023. Patients were divided into AKI group (n = 52) and non-AKI group (n = 118) based on the occurrence of AKI.
View Article and Find Full Text PDFEvaluation of acute substance abuse patients admitted to the ICU by different scoring system.
Toxicol Res (Camb)
February 2025
Department of Forensic Medicine and Clinical Toxicology, Champollion street, Faculty of Medicine, University of Alexandria, Alexandria, 21517, Egypt.
Drug abuse poisoning is a significant public health problem in many countries all over the world. The recent study aimed to assess the applicability of NEWS II and APACHE II scores as predictors of the ICU admission of acute intoxication by substance abuse. The study enrolled 98 patients admitted to the Alexandria Poison Center of the Main University Hospital (APC-AMUH) with a history of acute intoxication with substance abuse.
View Article and Find Full Text PDFGovernance of Indigenous data in open earth systems science.
Nat Commun
January 2025
Lands of the O'odham and Yaqui peoples, Native Nations Institute, Udall Center for Studies in Public Policy, University of Arizona, Tucson, AZ, USA.
In the age of big data and open science, what processes are needed to follow open science protocols while upholding Indigenous Peoples' rights? The Earth Data Relations Working Group (EDRWG), convened to address this question and envision a research landscape that acknowledges the legacy of extractive practices and embraces new norms across Earth science institutions and open science research. Using the National Ecological Observatory Network (NEON) as an example, the EDRWG recommends actions, applicable across all phases of the data lifecycle, that recognize the sovereign rights of Indigenous Peoples and support better research across all Earth Sciences.
View Article and Find Full Text PDFMachine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests-A Systematic Review.
Biomedicines
December 2024
Carol Davila University of Medicine and Pharmacy, Faculty of Medicine, General Surgery Department, University Emergency Hospital of Bucharest, 050098 Bucharest, Romania.
Sepsis presents significant diagnostic and prognostic challenges, and traditional scoring systems, such as SOFA and APACHE, show limitations in predictive accuracy. Machine learning (ML)-based predictive survival models can support risk assessment and treatment decision-making in the intensive care unit (ICU) by accounting for the numerous and complex factors that influence the outcome in the septic patient. A systematic literature review of studies published from 2014 to 2024 was conducted using the PubMed database.
View Article and Find Full Text PDFDesign of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning.
Heliyon
December 2024
Department of Computer Science & Engineering, K L E F Deemed To Be University, Green Fields, Vaddeswaram, Guntur (dt), Andhra Pradesh, 521230, India.
Real-time monitoring and anomaly detection are essential in healthcare to ensure safe conditions for patients and maintain the integrity of medical data samples. The majority of existing systems, despite improvements in healthcare technologies, cannot capture the spatial and temporal patterns of multimodal data simultaneously, process high Volume data in real-time, and ensure the privacy of patients' identity effectively. In this work, we handle these limitations by proposing a complete approach that uses state-of-the-art deep learning and data processing architectures to realize resilient anomaly detection in healthcare systems.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!