arXiv AI

Explainable AI for Chronic Kidney Disease Prediction Using Simulated Federated Learning

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 Machine Learning

Aug 4

Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation

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.

By Jingying Ma, Jinwei Wang, Lanlan Lu, Zhiqin Jiang, Mengling Feng, Feifei Zhang, Peng Shen, Yexiang Sun, Shenda Hong, Luxia Zhang

safety

arXiv AI

Jul 15

Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability

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.

By Mashrul Hossain, Nafesa Kibria, Fahim Shahriar

arXiv Machine Learning

22h ago

Population Health-Based Machine Learning Reveals Associations Between Psychosocial Factors and Chronic Kidney Disease

arXiv:2608. 17174v1 Announce Type: new Abstract: Chronic kidney disease (CKD) progresses silently and severely undermines quality of life, making early detection critical for improving patient outcomes.

By Md. Atik Shams, David Eisenberg, Sumaiya Fatema, Asma Sultana, D. M Hasibul Islam, Junnatul Mawa, Anindita Datta, Nafiya Ahmed, Danastan Tasaouf Mridula, SK. Sazid Mahmud, Simon Bin Akter, Tanjila Helaly, Jorge Fresneda Fernandez, Humayera Islam, Tanmoy Sarkar Pias

benchmarks

arXiv AI

Jun 29

Chronic Kidney Disease Prognosis Prediction Using Transformer

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.

By Yohan Lee, Dong Gyun Kang, SeHoon Park, Sa-Yoon Park, Kwangsoo Kim

llms nlp fine-tuning efficiency multimodal safety

arXiv Machine Learning

Aug 5

FedCARE: A Multi-Objective Personalised Federated Learning Framework for Smart Healthcare

arXiv:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.

By Rojalini Tripathy, Padmalochan Bera, Shreya Ghosh, Rajkumar Buyya

arXiv Machine Learning

Jul 27

A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data

arXiv:2509. 10517v3 Announce Type: replace Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use.

By Rodrigo Tertulino

benchmarks safety

arXiv Machine Learning

Jun 4

Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving

arXiv:2606. 04338v1 Announce Type: new Abstract: Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis.

By Xixi Tian, Di Wu, Xiang Liu, Yiziting Zhu, Yujie Li, Xin Shu, Bin Yi

safety

arXiv Machine Learning

Jun 5

Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease

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.

By Hamed Khosravi, Milad Khanchi, Mobina Noori, Srinjoy Das, Abdullah Al-Mamun, Imtiaz Ahmed

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 10

Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction

arXiv:2607. 08595v1 Announce Type: new Abstract: Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets.

By Hyunho Mo, Djura Smits, Mahlet A. Birhanu, Maarten J. G. Leening, Daniel Bos, Pim van der Harst, Esther E. Bron

safety

Hugging Face Trending Papers

Aug 4

FedCARE: A Multi-Objective Personalised Federated Learning Framework for Smart Healthcare

Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data. However, real-world healthcare federations are often characterised not only by non-IID data, but also by heterogeneous clinical objectives and partially overlapping feature spaces.

Hugging Face Trending Papers

Jul 9

Benchmark Evaluation of Feredated Learning on Multi-organ Images

The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI. Federated learning (FL) is a feasible approach to overcome these challenges.

benchmarks safety

arXiv Machine Learning

Jul 10

Classifier Chain-based Pathological Test Recommendation

arXiv:2607. 08299v1 Announce Type: new Abstract: Accurate and timely diagnoses are essential for quality patient care.

By Abu Rafe Md Jamil, Nayan Malakar

safety

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