Positive-Unlabeled (PU) Learning is a challenge presented by binary classification problems where there is an abundance of unlabeled data along with a small number of positive data instances, which can be used to address chronic disease screening problem. State-of-the-art PU learning methods have resulted in the development of various risk estimators, yet they neglect the differences among distinct populations. To address this issue, we present a novel Positive-Unlabeled Learning Tree (PUtree) algorithm. PUtree is designed to take into account communities such as different age or income brackets, in tasks of chronic disease prediction. We propose a novel approach for binary decision-making, which hierarchically builds community-based PU models and then aggregates their deliverables. Our method can explicate each PU model on the tree for the optimized non-leaf PU node splitting. Furthermore, a mask-recovery data augmentation strategy enables sufficient training of the model in individual communities. Additionally, the proposed approach includes an adversarial PU risk estimator to capture hierarchical PU-relationships, and a model fusion network that integrates data from each tree path, resulting in robust binary classification results. We demonstrate the superior performance of PUtree as well as its variants on two benchmarks and a new diabetes-prediction dataset.
Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease
eBrain Lab, Division of Engineering, New York University Abu Dhabi（纽约大学阿布扎比分校工程学院eBrain实验室）；Center for Quantum and Topological Systems, NYUAD Research Institute, New York University Abu Dhabi（纽约大学阿布扎比分校量子与拓扑系统中心）
Collaborative Management for Chronic Diseases and Depression: A Double Heterogeneity-based Multi-Task Learning Method
City University of Hong Kong（香港城市大学）；University of Science and Technology of China（中国科学技术大学）；University of Delaware（特拉华大学）；Hefei University of Technology（合肥工业大学）
Decision Support System for Chronic Diseases Based on Drug-Drug Interactions
The Chinese University of Hong Kong（香港中文大学）；Tencent AI Lab（腾讯人工智能实验室）；Hunan Normal University（湖南师范大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Deep Learning for Detecting and Early Predicting Chronic Obstructive Pulmonary Disease from Spirogram Time Series
Tianjin University of Technology（天津理工大学）；Tsinghua University（清华大学）；The Second Hospital of Tianjin Medical University（天津医科大学第二医院）；Peking University People’s Hospital（北京大学人民医院）；HeartVoice Medical Technology（声脉医疗科技）；University of Oxford（牛津大学）；Peking University（北京大学）
A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease
Universidad de Valladolid（巴利亚多利德大学）；Universidad de Salamanca（萨拉曼卡大学）；CIP Technologies LTD（CIP技术有限公司）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Gerencia Regional de Salud de Castilla y León（卡斯蒂利亚-莱昂地区卫生局）
Detecting Chronic Kidney Disease(CKD) at the Initial Stage: A Novel Hybrid Feature-selection Method and Robust Data Preparation Pipeline for Different ML Techniques
Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability
A Method for Characterizing Disease Progression from Acute Kidney Injury to Chronic Kidney Disease
Department of Biomedical Informatics, Columbia University（哥伦比亚大学生物医学信息学系）；Department of Medicine, Division of Nephrology, Columbia University（哥伦比亚大学医学系肾脏病科）；Department of Medicine, Division of General Medicine, Columbia University（哥伦比亚大学医学系全科医学）
Data-driven subgrouping of patient trajectories with chronic diseases: Evidence from low back pain
ETH Zurich（苏黎世联邦理工学院）；University of Southern Denmark（南丹麦大学）；Chiropractic Knowledge Hub（脊椎指压治疗知识中心）；Basel Academy for Quality and Research in Medicine（巴塞尔医学质量与研究学院）；University of Basel（巴塞尔大学）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence
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）
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）
Geometric and Quantum Kernel Methods for Predicting Skeletal Muscle Outcomes in chronic obstructive pulmonary disease
School of Computing Technologies, RMIT University（计算技术学院，拉筹纳斯大学）；School of Health & Biomedical Sciences, STEM College, RMIT University（健康与生物医学科学学院，STEM学院，拉筹纳斯大学）；Pattern Recognition Pty Ltd, Melbourne（模式识别有限公司，墨尔本）；Data61, CSIRO（Data61，澳大利亚联邦科学与工业研究组织）
A Functional Model for Structure Learning and Parameter Estimation in Continuous Time Bayesian Network: An Application in Identifying Patterns of Multiple Chronic Conditions
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）
Inferring Chronic Treatment Onset from ePrescription Data: A Renewal Process Approach
Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia（计算机与信息科学学院，卢布尔雅那大学，斯洛文尼亚）；Faculty of Public Administration, University of Ljubljana, SI-1000 Ljubljana, Slovenia（公共行政学院，卢布尔雅那大学，斯洛文尼亚）
