AIMC Topic: Acute Disease
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Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.
Computer methods and programs in biomedicine
Jul 29, 2024
BACKGROUND AND OBJECTIVES: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combinin...
Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.
ESC heart failure
Jul 9, 2024
AIMS: Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data...
Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches.
Scientific reports
Jul 9, 2024
Symptoms of Acute Respiratory infections (ARIs) among under-five children are a global health challenge. We aimed to train and evaluate ten machine learning (ML) classification approaches in predicting symptoms of ARIs reported by mothers among child...
Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study.
Journal of medical Internet research
Jul 3, 2024
BACKGROUND: Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability.
Multicenter validation of an artificial intelligence (AI)-based platform for the diagnosis of acute appendicitis.
Surgery
Jun 22, 2024
BACKGROUND: The current scores used to help diagnose acute appendicitis have a "gray" zone in which the diagnosis is usually inconclusive. Furthermore, the universal use of CT scanning is limited because of the radiation hazards and/or limited resour...
A deep learning-powered diagnostic model for acute pancreatitis.
BMC medical imaging
Jun 20, 2024
BACKGROUND: Acute pancreatitis is one of the most common diseases requiring emergency surgery. Rapid and accurate recognition of acute pancreatitis can help improve clinical outcomes. This study aimed to develop a deep learning-powered diagnostic mod...
Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa.
Frontiers in public health
Jun 19, 2024
BACKGROUND: Acute respiratory infections (ARIs) are the leading cause of death in children under the age of 5 globally. Maternal healthcare-seeking behavior may help minimize mortality associated with ARIs since they make decisions about the kind and...
Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians.
BMC emergency medicine
Jun 18, 2024
BACKGROUNDS: Acute Appendicitis (AA) is one of the most common surgical emergencies worldwide. This study aims to investigate the predictive performances of 6 different Machine Learning (ML) algorithms for simple and complicated AA.
Volumetric analysis of acute uncomplicated type B aortic dissection using an automated deep learning aortic zone segmentation model.
Journal of vascular surgery
Jun 6, 2024
BACKGROUND: Machine learning techniques have shown excellent performance in three-dimensional medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) using Society for Vascular Surgery (SVS) and Soci...
Computed Tomography Angiography
Aortic Aneurysm
Aortography
Radiographic Image Interpretation, Computer-Assisted
Automation
Aortic Dissection
Aortic Aneurysm, Thoracic
Time Factors
Imaging, Three-Dimensional
Predictive Value of Tests
Acute Disease
Humans
Retrospective Studies
Female
Reproducibility of Results
Aged
Male
Middle Aged
Disease Progression
Deep Learning
LDSG-Net: an efficient lightweight convolutional neural network for acute hypotensive episode prediction during ICU hospitalization.
Physiological measurement
Jun 5, 2024
. Acute hypotension episode (AHE) is one of the most critical complications in intensive care unit (ICU). A timely and precise AHE prediction system can provide clinicians with sufficient time to respond with proper therapeutic measures, playing a cr...
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