The array of complex and evolving patient data has limited clinical decision making in the emergency department (ED). This study introduces an advanced deep learning algorithm designed to enhance real-time prediction accuracy for integration into a n...
Machine Learning (ML) techniques require novel computer programming skills along with clinical domain knowledge to produce a useful model. We demonstrate the use of a cloud-based ML tool that does not require any programming expertise to develop, val...
Medical science monitor : international medical journal of experimental and clinical research
Dec 3, 2024
BACKGROUND Predicting 24-hour intraocular pressure (IOP) fluctuations is crucial for enhancing glaucoma management. Traditional methods of measuring 24-hour IOP fluctuations are complex and present certain limitations. The present study leverages mac...
INTRODUCTION: Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions for identifying tumor-derived...
INTRODUCTION: In Australia, almost 50 % of paramedics are female yet they remain under-represented in stereotypical depictions of the profession. The potentially transformative value of generative artificial intelligence (AI) may be limited by stereo...
PURPOSE: To evaluate the effect of lower field strength on quantitative apparent-diffusion-coefficient (ADC) values, contrast of the T2-weighted MR images and the performance of an AI-based segmentation.
Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons
Dec 2, 2024
BACKGROUND: Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication associated with the use of antiresorptive agents, impacting patient quality of life and treatment outcomes. Predictive modeling may aid in a better understandin...
International journal of medical informatics
Dec 2, 2024
BACKGROUND: The current congenital heart disease (CHD) prediction tools lack adequate interpretability and convenience, hindering the development of personalized CHD management strategies. We developed a machine learning-based risk stratification mod...
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