arXiv:2501. 16388v3 Announce Type: replace Abstract: Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health problem.
arXiv:2607. 11963v1 Announce Type: cross Abstract: The early detection of Chronic Kidney Disease using machine learning has attracted significant interest in healthcare-related computer science.
arXiv:2607. 25348v1 Announce Type: cross Abstract: Chronic Kidney Disease (CKD), characterized by the gradual loss of kidney function, remains a significant public health challenge.
arXiv:2511. 02340v3 Announce Type: replace Abstract: Chronic Kidney Disease (CKD) affects nearly 10\% of the global population and often progresses to end-stage renal failure.
arXiv:2607. 12260v1 Announce Type: new Abstract: Early screening of chronic kidney disease (CKD) is essential for preventing irreversible progression; however, many machine learning (ML)-based screening methods remain difficult to deploy in community and resource-limited screening settings due to their reliance on large labeled datasets, resource-intensive pathology tests, or high-dimensional clinical features, and limited robustness to population and distributional shifts.
arXiv:2601. 00175v2 Announce Type: replace Abstract: Objective: Develop and evaluate machine learning (ML) models for predicting incident liver cirrhosis (LC) one and two years prior to diagnosis using routinely collected electronic health record (EHR) data and benchmark their performance against the FIB-4 and APRI clinical scores.
arXiv:2403. 00965v2 Announce Type: replace-cross Abstract: Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the performance of machine learning models for early dialysis prediction.
arXiv:2607. 09165v1 Announce Type: cross Abstract: Achieving early and timely diagnosis and treatment for disease is a major challenge.
arXiv:2606. 06516v1 Announce Type: cross Abstract: We advance a new probabilistic supervised learning approach that permits reliable, automated, and early individualised prediction of the severity with which a disease will develop in a prospective patient.
arXiv:2511. 16839v4 Announce Type: replace-cross Abstract: Purpose: Heart failure (HF) discharge planning depends on identifying patients at risk of deterioration or death, yet accurate prediction from routinely collected electronic health records (EHRs) remains challenging.
arXiv:2607. 15721v1 Announce Type: new Abstract: Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular disease frequently co-occur and share metabolic, vascular, demographic, and behavioral determinants.
arXiv:2605. 12895v2 Announce Type: replace-cross Abstract: Clinical decision-support systems are expert systems whose recommendations clinicians act on directly, yet they are usually cleared on one aggregate accuracy number from a held-out test set.
arXiv:2607. 20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge, especially for smaller locally deployable models.