AIMC Topic: ROC Curve
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Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.
Scientific reports
Apr 3, 2025
Chronic kidney disease-mineral bone disorder is a common complication in patients with chronic kidney disease (CKD) and end-stage kidney disease (ESKD), and it increases the risk of osteoporosis and fractures. This study aimed to develop predictive m...
Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.
Scientific reports
Apr 3, 2025
Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making. Existing studies have predominantly focused on EEG for assessing brain injury, with some exploring...
Pancreatic Cancer Detection and Differentiation from Chronic Pancreatitis: Potential Biomarkers Identified through a High-Throughput Multiplex Proteomic Assay and Machine Learning-Based Analysis.
Annals of laboratory medicine
Apr 2, 2025
BACKGROUND: Pancreatic cancer (PC)-screening methods have limited accuracy despite their high clinical demand. Differential diagnosis of chronic pancreatitis (CP) poses another challenge for PC diagnosis. Therefore, we aimed to identify blood protein...
Identifying the Most Critical Predictors of Workplace Violence Experienced by Junior Nurses: An Interpretable Machine Learning Perspective.
Journal of nursing management
Apr 2, 2025
Workplace violence, defined as any disruptive behavior or threat to employees, seriously threatens junior nurses. Compared with senior nurses, junior nurses are more vulnerable to workplace violence due to inexperience, low professional recognition,...
Application of an Automated Deep Learning Program to A Diagnostic Classification Model: Differentiating High-Risk Adenomas Among Colorectal Polyps 10 mm or Smaller.
Journal of digestive diseases
Apr 2, 2025
OBJECTIVE: This study aimed to develop a computer-aided diagnosis (CADx) model using an automated deep learning (DL) program to classify low- and high-risk adenomas among colorectal polyps ≤ 10 mm with standard white-light endoscopy.
Development and validation of machine learning models for early diagnosis and prognosis of lung adenocarcinoma using miRNA expression profiles.
Cancer biomarkers : section A of Disease markers
Apr 2, 2025
ObjectiveStudy aims to develop diagnostic and prognostic models for lung adenocarcinoma (LUAD) using Machine learning（ML）algorithms, aiming to enhance clinical decision-making accuracy.MethodsData from The Cancer Genome Atlas (TCGA) for LUAD patients...
90-day mortality prediction in elective visceral surgery using machine learning: a retrospective multicenter development, validation, and comparison study.
International journal of surgery (London, England)
Apr 1, 2025
BACKGROUND: Machine Learning (ML) is increasingly being adopted in biomedical research, however, its potential for outcome prediction in visceral surgery remains uncertain. This study compares the potential of ML methods for preoperative 90-day morta...
Using 3D Convolutional Neural Network and Corvis ST Corneal Dynamic Video for Detecting Forme Fruste Keratoconus.
Journal of refractive surgery (Thorofare, N.J. : 1995)
Apr 1, 2025
PURPOSE: To evaluate the performance of a three-dimensional convolutional neural network (3D CNN) in detecting forme fruste keratoconus (FFKC).
Comparative analysis of deep learning architectures for thyroid eye disease detection using facial photographs.
BMC ophthalmology
Apr 1, 2025
PURPOSE: To compare two artificial intelligence (AI) models, residual neural networks ResNet-50 and ResNet-101, for screening thyroid eye disease (TED) using frontal face photographs, and to test these models under clinical conditions.
Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.
PloS one
Apr 1, 2025
BACKGROUND: Non-small-cell lung cancer (NSCLC) and its surgery significantly increase the venous thromboembolism (VTE) risk. This study explored the VTE risk factors and established a machine-learning model to predict a failure of postoperative throm...
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