AIMC Topic: ROC Curve
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Development of a machine learning-based risk model for postoperative complications of lung cancer surgery.
Surgery today
Jun 19, 2024
PURPOSE: To develop a comorbidity risk score specifically for lung resection surgeries.
Machine Learning-Based Models for Advanced Fibrosis and Cirrhosis Diagnosis in Chronic Hepatitis B Patients With Hepatic Steatosis.
Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
Jun 19, 2024
BACKGROUND AND AIMS: The global rise of chronic hepatitis B (CHB) superimposed on hepatic steatosis (HS) warrants noninvasive, precise tools for assessing fibrosis progression. This study leveraged machine learning (ML) to develop diagnostic models f...
Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage.
EBioMedicine
Jun 19, 2024
BACKGROUND: Cerebral vasospasm (CV) is a feared complication which occurs after 20-40% of subarachnoid haemorrhage (SAH). It is standard practice to admit patients with SAH to intensive care for an extended period of resource-intensive monitoring. We...
A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.
Head & neck
Jun 18, 2024
BACKGROUND: To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyng...
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.
Deep Learning for Echo Analysis, Tracking, and Evaluation of Mitral Regurgitation (DELINEATE-MR).
Circulation
Jun 17, 2024
BACKGROUND: Artificial intelligence, particularly deep learning (DL), has immense potential to improve the interpretation of transthoracic echocardiography (TTE). Mitral regurgitation (MR) is the most common valvular heart disease and presents unique...
Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis.
RMD open
Jun 17, 2024
OBJECTIVES: To train, test and validate the performance of a convolutional neural network (CNN)-based approach for the automated assessment of bone erosions, osteitis and synovitis in hand MRI of patients with inflammatory arthritis.
Patient stratification based on the risk of severe illness in emergency departments through collaborative machine learning models.
The American journal of emergency medicine
Jun 15, 2024
OBJECTIVES: Emergency department (ED) overcrowding presents a global challenge that inhibits prompt care for critically ill patients. Traditional 5-level triage system that heavily rely on the judgment of the triage staff could fail to detect subtle ...
Machine Learning-Based Critical Congenital Heart Disease Screening Using Dual-Site Pulse Oximetry Measurements.
Journal of the American Heart Association
Jun 15, 2024
BACKGROUND: Oxygen saturation (Spo) screening has not led to earlier detection of critical congenital heart disease (CCHD). Adding pulse oximetry features (ie, perfusion data and radiofemoral pulse delay) may improve CCHD detection, especially coarct...
Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction.
Internal and emergency medicine
Jun 14, 2024
This study aimed to develop several new machine learning models based on hibernating myocardium to predict the major adverse cardiac events(MACE) of ischemic left ventricular systolic dysfunction(LVSD) patients receiving either percutaneous coronary ...
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