Chronic kidney disease (CKD) has a poor prognosis due to excessive risk factors and comorbidities associated with it. The early detection of CKD faces challenges of insufficient medical histories of positive patients and complicated risk factors. In this paper, we propose the TRACE (Transformer-RNN Autoencoder-enhanced CKD Detector) framework, an end-to-end prediction model using patients' medical history data, to deal with these challenges. TRACE presents a comprehensive medical history representation with a novel key component: a Transformer-RNN autoencoder. The autoencoder jointly learns a medical concept embedding via Transformer for each hospital visit, and a latent representation which summarizes a patient's medical history across all the visits. We compared TRACE with multiple state-of-the-art methods on a dataset derived from real-world medical records. Our model has achieved 0.5708 AUPRC with a 2.31% relative improvement over the best-performing method. We also validated the clinical meaning of the learned embeddings through visualizations and a case study, showing the potential of TRACE to serve as a general disease prediction model.
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）
University at Buffalo（布法罗大学）；Department of Computer Science and Engineering University at Buffalo（布法罗大学计算机科学与工程系）
We study the behavior of a Time-Aware Long Short-Term Memory Autoencoder, a state-of-the-art method, in the context of learning latent representations from irregularly sampled patient data. We identify a key issue in the way such recurrent neural network models are being currently used and show that the solution of the issue leads to significant improvements in the learnt representations on both synthetic and real datasets. A detailed analysis of the improved methodology for representing patients suffering from Chronic Kidney Disease (CKD) using clinical data is provided. Experimental results …
Nigerian Defence Academy（尼日利亚国防学院）；University of Maiduguri（迈杜古里大学）；Federal University Dutsinma（杜钦马联邦大学）
Deep neural Network (DNN) is becoming a focal point in Machine Learning research. Its application is penetrating into different fields and solving intricate and complex problems. DNN is now been applied in health image processing to detect various ailment such as cancer and diabetes. Another disease that is causing threat to our health is the kidney disease. This disease is becoming prevalent due to substances and elements we intake. Death is imminent and inevitable within few days without at least one functioning kidney. Ignoring the kidney malfunction can cause chronic kidney disease leading…
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）
University of Jammu（查谟大学）；Government Medical College（政府医学院）
Areas where Artificial Intelligence (AI) & related fields are finding their applications are increasing day by day, moving from core areas of computer science they are finding their applications in various other domains.In recent times Machine Learning i.e. a sub-domain of AI has been widely used in order to assist medical experts and doctors in the prediction, diagnosis and prognosis of various diseases and other medical disorders. In this manuscript the authors applied various machine learning algorithms to a problem in the domain of medical diagnosis and analyzed their efficiency in predict…
University of Virginia（弗吉尼亚大学）；University of California Los Angeles（加州大学洛杉矶分校）
Chronic kidney disease (CKD) is a gradual loss of renal function over time, and it increases the risk of mortality, decreased quality of life, as well as serious complications. The prevalence of CKD has been increasing in the last couple of decades, which is partly due to the increased prevalence of diabetes and hypertension. To accurately detect CKD in diabetic patients, we propose a novel framework to learn sparse longitudinal representations of patients' medical records. The proposed method is also compared with widely used baselines such as Aggregated Frequency Vector and Bag-of-Pattern in…
Chronic Kidney Disease (CKD) has infected almost 800 million people around the world. Around 1.7 million people die each year because of it. Detecting CKD in the initial stage is essential for saving millions of lives. Many researchers have applied distinct Machine Learning (ML) methods to detect CKD at an early stage, but detailed studies are still missing. We present a structured and thorough method for dealing with the complexities of medical data with optimal performance. Besides, this study will assist researchers in producing clear ideas on the medical data preparation pipeline. In this …
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）
Chronic kidney disease (CKD) represents a slowly progressive disorder that can eventually require renal replacement therapy (RRT) including dialysis or renal transplantation. Early identification of patients who will require RRT (as much as 1 year in advance) improves patient outcomes, for example by allowing higher-quality vascular access for dialysis. Therefore, early recognition of the need for RRT by care teams is key to successfully managing the disease. Unfortunately, there is currently no commonly used predictive tool for RRT initiation. In this work, we present a machine learning model…
Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability
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）
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）
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）
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）
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）
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）
Amazon Web Services Inc（亚马逊网络服务公司）；Cerner Corporation（塞纳公司）
Chronic disease progression is emerging as an important area of investment for healthcare providers. As the quantity and richness of available clinical data continue to increase along with advances in machine learning, there is great potential to advance our approaches to caring for patients. An ideal approach to this problem should generate good performance on at least three axes namely, a) perform across many clinical conditions without requiring deep clinical expertise or extensive data scientist effort, b) generalization across populations, and c) be explainable (model interpretability). W…
Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce the complications of AKI. We developed data-driven prediction models to estimate the risk of new AKI onset. We generated models from clinical notes within the first 24 hours following intensive care unit (ICU) admission extracted from Medical Information Mart for Intensive Care III (MIMIC-III). From the clinical notes, we generated clinically meaningful word…
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）
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）
Machine Learning-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database
University of Southern California（南加州大学）；Iran University of Medical Sciences（伊朗医学科学大学）；California State University（加利福尼亚州立大学）；California State Polytechnic University（加利福尼亚州_polytechnic大学）
Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation
Peking University（北京大学）；National University of Singapore（新加坡国立大学）；Peking University First Hospital（北京大学第一医院）；Chinese Academy of Medical Sciences（中国医学科学院）；Ningbo Yinzhou No.2 Hospital（宁波市鄞州第二医院）；Yinzhou District Center for Disease Control and Prevention（鄞州区疾病预防控制中心）
Privacy-Preserving Generative Modeling and Clinical Validation of Longitudinal Health Records for Chronic Disease
Vironix Health Inc（Vironix健康公司）；University of Oxford（牛津大学）；University of Cambridge（剑桥大学）；Stanford University（斯坦福大学）；University of Southern California（南加州大学）
Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, however, focus on structured fields and lose the vast amount of information inthe unstructured notes. In this work we propose a general multi-task framework for disease onsetprediction that combines both free-text medical notes and structured information. We compareperformance of d…
Massachusetts Institute of Technology（麻省理工学院）；National Cheng Kung University（国立成功大学）
In many domains such as medicine, training data is in short supply. In such cases, external knowledge is often helpful in building predictive models. We propose a novel method to incorporate publicly available domain expertise to build accurate models. Specifically, we use word2vec models trained on a domain-specific corpus to estimate the relevance of each feature's text description to the prediction problem. We use these relevance estimates to rescale the features, causing more important features to experience weaker regularization. We apply our method to predict the onset of five chronic di…
Kyoto University（京都大学）；Kyowa Hakko Bio Co., Ltd.（协和发酵生物株式会社）；Yokohama City University（横滨市立大学）；Hirosaki University（弘前大学）
Early disease detection and prevention methods based on effective interventions are gaining attention. Machine learning technology has enabled precise disease prediction by capturing individual differences in multivariate data. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex health factors are involved in the development of chronic diseases. However, it remains a challenge to identify individual physiological state changes in cross-disease onset processes because of the complex relationships among multipl…
Characterization of a patient clinical phenotype is central to biomedical informatics. ICD codes, assigned to inpatient encounters by coders, is important for population health and cohort discovery when clinical information is limited. While ICD codes are assigned to patients by professionals trained and certified in coding there is substantial variability in coding. We present a methodology that uses deep learning methods to model coder decision making and that predicts ICD codes. Our approach predicts codes based on demographics, lab results, and medications, as well as codes from previous e…
Electronic health records represent a holistic overview of patients' trajectories. Their increasing availability has fueled new hopes to leverage them and develop accurate risk prediction models for a wide range of diseases. Given the complex interrelationships of medical records and patient outcomes, deep learning models have shown clear merits in achieving this goal. However, a key limitation of these models remains their capacity in processing long sequences. Capturing the whole history of medical encounters is expected to lead to more accurate predictions, but the inclusion of records coll…
Febrile neutropenia (FN) has been associated with high mortality, especially among adults with cancer. Understanding the patient and provider level heterogeneity in FN hospital admissions has potential to inform personalized interventions focused on increasing survival of individuals with FN. We leverage machine learning techniques to disentangling the complex interactions among multi domain risk factors in a population with FN. Data from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample and Nationwide Inpatient Sample (NIS) were used to build machine learning based …
Mutual Information Assisted Ensemble Recommender System for Identifying Critical Risk Factors in Healthcare Prognosis
Bethune College, University of Calcutta（加尔各答大学贝休恩学院）；Jadavpur University（jadavpur大学）；Mahindra University（马恒达大学）；Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）；King Mongkut’s University of Technology Thonburi（泰国国王科技大学吞武里校区）
Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction
Insight Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland（数据分析洞察中心、计算学院、都柏林城市大学，都柏林，爱尔兰）；Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam（信息技术学院、胡志明市开放大学，胡志明市，越南）；Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City, Vietnam（电气与电子工程学院、胡志明市交通大学，胡志明市，越南）；School of Public Health, University College Cork, Cork, Ireland（公共卫生学院、科克大学学院，科克，爱尔兰）
