arXiv Machine Learning

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.

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 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 AI

Jul 29

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.

By Md Zahid Hasan Ontor, Md Al Amin, Anik Dev Nath, Bikash Kumar Paul

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 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

Jul 15

From Many to Meaningful: Feature-Guided Zero-Shot Chronic Kidney Disease Screening Using Large Language Models

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.

By Muhammad Ashad Kabir, Sirajam Munira

llms

arXiv AI

Jul 1

Can Physician Expertise Improve Machine Learning Identification of Delirium?

arXiv:2606. 30651v1 Announce Type: cross Abstract: Delirium is common in hospitalized patients and is often missed in routine care.

By Xinyu Qin, Vicky Ye, Ruiheng Yu, Lu Wang

llms

arXiv AI

Jul 13

A Personalized Computational Framework for Assessing the Sufficiency of Partially Observed Data in Healthcare AI models

arXiv:2607. 09165v1 Announce Type: cross Abstract: Achieving early and timely diagnosis and treatment for disease is a major challenge.

By Qingchu Jin, Felistas Mazhude, Jamie B. Rabb, Robert S. Kramer, Douglas B. Sawyer, Raimond L. Winslow

arXiv Machine Learning

Jun 2

Early Prediction of Liver Cirrhosis Up to Two Years in Advance: A Machine Learning Study Benchmarking Against the FIB-4 and APRI Scores

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.

By Zhuqi Miao, Ahmed G Qasem, Sujan Ravi, Jason T. Cheng, Abdulaziz Ahmed, Courtney W. Houchen, Sumayah Abed, Dilorom Azimdjanovna Zuparova, Abdulaziz Ahmed

benchmarks

arXiv Machine Learning

Jul 14

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts

arXiv:2607. 11656v1 Announce Type: cross Abstract: Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data.

By Christelle Schneuwly Diaz, Narmina Baghirova, Duy-Thanh Vu, Duy-Cat Can, Gilles Allali, Philippe Ryvlin, Oliver Y. Ch\'en

llms multimodal safety

arXiv AI

Jun 2

Towards a General Intelligence and Interface for Wearable Health Data

arXiv:2605. 22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging.

By Girish Narayanswamy, Maxwell A. Xu, A. Ali Heydari, Samy Abdel-Ghaffar, Marius Guerard, Kara Vaillancourt, Zhihan Zhang, Jake Garrison, Levi Albuquerque, Dimitris Spathis, Hong Yu, Hamid Palangi, Xuhai "Orson" Xu, David G. T. Barrett, Joseph Breda, Jed McGiffin, Yubin Kim, Yuwei Zhang, Naghmeh Rezaei, Samuel Solomon, Karan Ahuja, Tim Althoff, Jake Sunshine, Ming-Zher Poh, Benjamin Yetton, Ari Winbush, Nicholas B. Allen, James M. Rehg, Isaac Galatzer-Levy, Yun Liu, John Hernandez, Anupam Pathak, Conor Heneghan, Yuzhe Yang, Ahmed A. Metwally, Pushmeet Kohli, Mark Malhotra, Shwetak Patel, Xin Liu, Daniel McDuff

llms rag agents

Hugging Face Trending Papers

Aug 4

Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants

There has been significant interest in using machine learning algorithms to predict kidney transplant outcomes, such as the number of years until a graft inevitably fails. These prediction algorithms could possibly be used for pre-transplant donor-recipient matching to identify more compatible donors and recipients and thus improve post-transplant outcomes.

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