AIMC Topic: Area Under Curve
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Explainable machine learning for early prediction of sepsis in traumatic brain injury: A discovery and validation study.
PloS one
Nov 11, 2024
BACKGROUND: People with traumatic brain injury (TBI) are at high risk for infection and sepsis. The aim of the study was to develop and validate an explainable machine learning(ML) model based on clinical features for early prediction of the risk of ...
Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry.
Scientific reports
Nov 7, 2024
Clinical risk scores that predict outcomes in patients with atrial fibrillation (AF) have modest predictive value. Machine learning (ML) may achieve greater results when predicting adverse outcomes in patients with recently diagnosed AF. Several ML m...
An improved AlexNet deep learning method for limb tumor cancer prediction and detection.
Biomedical physics & engineering express
Nov 5, 2024
Synovial sarcoma (SS) is a rare cancer that forms in soft tissues around joints, and early detection is crucial for improving patient survival rates. This study introduces a convolutional neural network (CNN) using an improved AlexNet deep learning c...
Prediction of the risk of mortality in older patients with coronavirus disease 2019 using blood markers and machine learning.
Frontiers in immunology
Nov 1, 2024
INTRODUCTION: The mortality rate among older people infected with severe acute respiratory syndrome coronavirus 2 is alarmingly high. This study aimed to explore the predictive value of a novel model for assessing the risk of death in this vulnerable...
Assessment of machine learning classifiers for predicting intraoperative blood transfusion in non-cardiac surgery.
Transfusion clinique et biologique : journal de la Societe francaise de transfusion sanguine
Oct 18, 2024
BACKGROUND: This study aimed to develop a machine learning classifier for predicting intraoperative blood transfusion in non-cardiac surgeries.
Pathologist-level diagnosis of ulcerative colitis inflammatory activity level using an automated histological grading method.
International journal of medical informatics
Oct 9, 2024
BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) is a global disease that is evolving with increasing incidence. However, there are few works on computationally assisted diagnosis of IBD based on pathological images. Therefore, based on the UK a...
Adapting Action Recognition Neural Networks for Automated Infantile Spasm Detection.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Oct 9, 2024
Infantile spasms are a severe epileptic syndrome characterized by short muscular contractions lasting from 0.5 to 2 seconds. They are often misdiagnosed due to their atypical presentation, and treatment is frequently delayed, leading to stagnation or...
Prediction of pre-eclampsia with machine learning approaches: Leveraging important information from routinely collected data.
International journal of medical informatics
Oct 5, 2024
BACKGROUND: Globally, pre-eclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality. PE prediction using routinely collected data has the advantage of being widely applicable, particularly in low-resource settings. Early int...
Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.
Archives of Iranian medicine
Oct 1, 2024
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. T...
A Compact Graph Convolutional Network With Adaptive Functional Connectivity for Seizure Prediction.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 23, 2024
Seizure prediction using EEG has significant implications for the daily monitoring and treatment of epilepsy patients. However, the task is challenging due to the underlying spatiotemporal correlations and patient heterogeneity. Traditional methods o...
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