Studies in health technology and informatics
Aug 22, 2024
Brain tumours are the most commonly occurring solid tumours in children, albeit with lower incidence rates compared to adults. However, their inherent heterogeneity, ethical considerations regarding paediatric patients, and difficulty in long-term fo...
Studies in health technology and informatics
Aug 22, 2024
Emergency departments (EDs) are pivotal in detecting child abuse and neglect, but this task is often complex. Our study developed a machine learning model using structured and unstructured electronic health record (EHR) data to predict when children ...
Studies in health technology and informatics
Aug 22, 2024
This paper presents a comprehensive workflow for integrating revolving events into the transitive sequential pattern mining (tSPM+) algorithm and Machine Learning for Health Outcomes (MLHO) framework, emphasizing best practices and pitfalls in its ap...
Studies in health technology and informatics
Aug 22, 2024
Personalized medicine enables precise tumor treatment for a patient's molecular genetic profile. To devise optimal targeted treatment plans for patients in a molecular tumor board, physicians must consider alterations on gene- and proteins levels but...
Studies in health technology and informatics
Aug 22, 2024
Patients who do not show up for scheduled appointments are a considerable cost and concern in healthcare. In this study, we predict patient no-shows for outpatient surgery at the endoscopy ward of a hospital in Denmark. The predictions are made by tr...
Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Aug 21, 2024
Fracture prediction is essential in managing patients with osteoporosis and is an integral component of many fracture prevention guidelines. We aimed to identify the most relevant clinical fracture risk factors in contemporary populations by training...
The potential of serum extracellular vesicles (EVs) as non-invasive biomarkers for diagnosing colorectal cancer (CRC) remains elusive. We employed an in-depth 4D-DIA proteomics and machine learning (ML) pipeline to identify key proteins, PF4 and AACT...
INTRODUCTION: Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g., 50th-percen...
To develop machine learning models based on preoperative dynamic enhanced magnetic resonance imaging (DCE-MRI) radiomics and to explore their potential prognostic value in the differential diagnosis of human epidermal growth factor receptor 2 (HER2)-...
It may be difficult to distinguish between enchondroma and low-grade malignant cartilage tumors (grade 1) radiologically. This study aimed to construct machine learning models using 3D computed tomography (CT)-based radiomics analysis to differentiat...
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