Waste disposal in landfills remains a global concern. Despite technological developments, landfill leachate poses a hazard to ecosystems and human health since it acts as a secondary reservoir for legacy and emerging pollutants. This study provides a...
OBJECTIVE: Deep learning algorithms have commonly been used for the differential diagnosis between benign and malignant thyroid nodules. The aim of the study described here was to develop an integrated system that combines a deep learning model and a...
BACKGROUND: Accurately predicting survival in patients with cancer is crucial for both clinical decision-making and patient counseling. The primary aim of this study was to generate the first machine-learning algorithm to predict the risk of mortalit...
BACKGROUND: Early diagnosis of atrial fibrillation (AF) is important for preventing stroke and other complications. Predicting AF risk in advance can improve early diagnostic efficiency. Deep learning has been used for disease risk prediction; howeve...
This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the common challenge of limited datasets in health-related Machine Learning (ML) applications, we use data...
Ovarian cancer (OC), known for its pronounced heterogeneity, has long evaded a unified classification system despite extensive research efforts. This study integrated five distinct multi-omics datasets from eight multicentric cohorts, applying a comb...
Clinical orthopaedics and related research
Mar 12, 2024
BACKGROUND: Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling a...
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