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Individual risk and prognostic value prediction by interpretable machine learning for distant metastasis in neuroblastoma: A population-based study and an external validation.

International journal of medical informatics
PURPOSE: Neuroblastoma (NB) is a childhood malignancy with a poor prognosis and a propensity for distant metastasis (DM). We aimed to establish machine learning (ML) based model to accurately predict risk of DM and prognosis of NB patients with DM.

Graph Neural Network Learning on the Pediatric Structural Connectome.

Tomography (Ann Arbor, Mich.)
PURPOSE: Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain graph that has proven useful for studying cognitive function and impairment. While graph neural networks (GNNs), s...

Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing.

Parasites & vectors
BACKGROUND: Amebiasis represents a significant global health concern. This is especially evident in developing countries, where infections are more common. The primary diagnostic method in laboratories involves the microscopy of stool samples. Howeve...

Feasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis.

European radiology experimental
BACKGROUND: Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. This study explores ...

Development and validation of machine learning-based prediction model for central venous access device-related thrombosis in children.

Thrombosis research
BACKGROUND: Identifying independent risk factors and implementing high-quality assessment tools for early detection of patients at high risk of central venous access device (CVAD)-related thrombosis (CRT) plays a critical role in delivering timely pr...

Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning.

BMC medical informatics and decision making
INTRODUCTION: Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).

A pediatric emergency prediction model using natural language process in the pediatric emergency department.

Scientific reports
This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. It analyzed 87,759 pediatric cases from a South Korean tertiary hospital (2012-2021...

Deep learning based tractography with TractSeg in patients with hemispherotomy: Evaluation and refinement.

NeuroImage. Clinical
Deep learning-based tractography implicitly learns anatomical prior knowledge that is required to resolve ambiguities inherent in traditional streamline tractography. TractSeg is a particularly widely used example of such an approach. Even though it ...

Prediction of surgical necessity in children with ureteropelvic junction obstruction using machine learning.

Irish journal of medical science
BACKGROUND: Hydronephrosis developing at the ureteropelvic junction due to obstruction poses clinical challenges as it has the potential to cause renal damage.

Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery.

Scientific reports
With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, surgical decision-making and optimal strategies still lack standardization and personalized customiz...