With Polycystic Kidney Disease (PKD) potentially leading to fatal complications in patients due to the formation of cysts in kidneys, early detection of PKD is crucial for effective management of the condition. However, the various patient-specific factors that play a role in the diagnosis make it an intricate puzzle for clinicians to solve, leading to possible kidney failure. Therefore, in this study we aim to utilize a deep learning-based approach for early disease detection through gene expression analysis. The devised neural network is able to achieve accurate and robust prediction results for possible PKD in kidneys, thereby improving patient outcomes. Furthermore, by conducting a gene ontology analysis, we were able to predict the top gene processes and functions that PKD may affect.
Fairness-aware kidney exchange and kidney paired donation
Division of Biostatistics, University of California, Berkeley（加州大学伯克利分校生物统计学系）；Department of Statistics and Data Science and Department of Biostatistics, University of California, Los Angeles（加州大学洛杉矶分校统计与数据科学系和生物统计学系）；University of California, Berkeley（加州大学伯克利分校）
Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records
Centre for Big Data Research in Health (CBDRH)（健康大数据研究中心）；The University of New South Wales（新南威尔士大学）
Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injury
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Department of Nephrology, TEDA Hospital, Tianjin University（天津大学TEDA医院肾内科）；Tianjin Medical University General Hospital（天津医科大学总医院）；Department of Nephrology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine（天津中医药大学第一教学医院肾内科）
NORA: A Nephrology-Oriented Representation Learning Approach Towards Chronic Kidney Disease Classification
Florida Institute of Technology（佛罗里达理工学院）；University of California Riverside（加州大学河滨分校）；Edgewood Jr./Sr. High School（埃德伍德高中）；Shadan Institute of Medicine Sciences（沙达医学科学研究所）；Riverside Nephrology Physicians（拉维斯德肾病医生）
Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease
Department of Health Policy, Stanford University（健康政策系，斯坦福大学）；Stanford University（斯坦福大学）；Department of Epidemiology and Population Health, Stanford University（流行病学与人口健康系，斯坦福大学）
Transformer-based Time-to-Event Prediction for Chronic Kidney Disease Deterioration
Faculty of Data and Decision Sciences, Technion-Israel Institute of Technology（以色列理工学院数据与决策科学学院）；Faculty of Biology, Technion-Israel Institute of Technology（以色列理工学院生物学院）；The Taub Faculty of Computer Science, Technion-Israel Institute of Technology（以色列理工学院陶布计算机科学学院）
From Many to Meaningful: Feature-Guided Zero-Shot Chronic Kidney Disease Screening Using Large Language Models
School of Computing, Mathematics and Engineering, Charles Sturt University（查尔斯·斯特尔特大学计算、数学与工程学院）；Department of Computer Science, Rensselaer Polytechnic Institute（伦斯勒理工学院计算机科学系）
Automated Dynamic Bayesian Networks for Predicting Acute Kidney Injury Before Onset
University of California, Los Angeles（加州大学洛杉矶分校）；UCLA Clinical and Translational Science Institute（加州大学洛杉矶分校临床与转化科学研究所）；David Geffen School of Medicine at UCLA（加州大学洛杉矶分校大卫·格芬医学院）；Providence Health Care（普罗维登斯医疗保健系统）
Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings
School of Computing, Mathematics and Engineering, Charles Sturt University（查尔斯·斯特特大学计算、数学与工程学院）；Department of Computer Science, Rensselaer Polytechnic Institute（伦斯勒理工学院计算机科学系）；Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b)（孟加拉国国际腹泻病研究中心卫生系统与人口研究部）；Technical Training Unit, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b)（孟加拉国国际腹泻病研究中心技术培训部）
Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease
Department of Industrial & Management Systems Engineering, West Virginia University（威斯康星大学工业与管理系统工程系）；Department of Electrical and Computer Engineering, Concordia University（康科迪亚大学电气与计算机工程系）；Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）；School of Mathematical & Data Sciences, West Virginia University（威斯康星大学数学与数据科学学院）；School of Systems Science and Industrial Engineering, The State University of New York at Binghamton（纽约州立大学布法罗分校系统科学与工业工程学院）；H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology（佐治亚理工学院H.米尔顿·斯图尔特工业与系统工程学院）
Assessing the generalization performance of SAM for ureteroscopy scene understanding
Tecnologico de Monterrey, School of Engineering and Sciences（墨西哥技术学院，工程与科学学院）；CRAN UMR 7039, Université de Lorraine and CNRS（洛林大学CRAN UMR 7039及CNRS）；CHRU de Nancy-Brabois, service d’urologie（南布拉欧斯医院泌尿科）
NephroNet: A Novel Program for Identifying Renal Cell Carcinoma and Generating Synthetic Training Images with Convolutional Neural Networks and Diffusion Models
Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care
Tsinghua University（清华大学）；Institute of Brain and Cognitive Sciences, Tsinghua University（清华大学脑与认知科学研究院）；Chinese PLA General Hospital（中国人民解放军总医院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
