AIMC Topic: ROC Curve

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Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis.

BMC pulmonary medicine
BACKGROUND: Endobronchial ultrasound (EBUS) is a widely used imaging modality for evaluating thoracic lymph nodes (LNs), particularly in the staging of lung cancer. Artificial intelligence (AI)-assisted EBUS has emerged as a promising tool to enhance...

Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana.

BMC pregnancy and childbirth
BACKGROUND: Machine learning algorithms may contribute to improving maternal and child health, including determining the suitability of caesarean section (CS) births in low-resource countries. Despite machine learning algorithms offering a more robus...

Identification of MEG3 and MAPK3 as potential therapeutic targets for osteoarthritis through multiomics integration and machine learning.

Scientific reports
Knee osteoarthritis (KOA) is a prevalent degenerative joint disorder, yet its underlying molecular mechanisms remain puzzling. This study aimed to uncover the genes with a causal relationship to KOA using Mendelian randomization (MR), transcriptomic ...

False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions.

Scientific reports
As collaborative Machine Learning on cross-institutional, fully distributed networks become an important tool in predictive health modeling, its inherent security risks must be addressed. One among such risks is the lack of a mitigation strategy agai...

Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.

Scientific reports
To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracranial hypertension (IH) following primary decompressive craniectomy (DC) in patients with severe traumatic brain injury (TBI) and to demonstrate its pote...

Key factors in predictive analysis of cardiovascular risks in public health.

Scientific reports
This research emphasizes the role of analytics in evaluating the risk of disease (CVD) focusing on thorough data preparation and feature engineering for accurate predictions. We studied machine learning (ML) and learning (DL) models, such as Logistic...

Integrating bioinformatics and machine learning to investigate the mechanisms by which three major respiratory infectious diseases exacerbate heart failure.

Scientific reports
Heart failure (HF) is a severe cardiovascular disease often worsened by respiratory infections like influenza, COVID-19, and community-acquired pneumonia (CAP). This study aims to uncover the molecular commonalities among these respiratory diseases a...

Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging.

Scientific reports
Early diagnosis of Neurological Disorders (ND) such as Alzheimer's disease (AD) and Brain Tumors (BT) can be highly challenging since these diseases cause minor changes in the brain's anatomy. Magnetic Resonance Imaging (MRI) is a vital tool for diag...

Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.

Scientific reports
This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency department (ED). We conducted a retrospective study analyzing adult ED visits at a tertiary hospital. ...

Automated grading of rectocele with an MRI radiomics model.

Scientific reports
To develop an automated grading model for rectocele (RC) based on radiomics and evaluate its efficacy. This study retrospectively analyzed a total of 9,392 magnetic resonance imaging (MRI) images obtained from 222 patients who underwent dynamic magne...