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 Sequences on real EHR data, and the experimental results indicate that the proposed model achieves higher predictive performance. Additionally, the learned representations are interpreted and visualized to bring clinical insights.
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 …
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…
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…
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 …
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…
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 …
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）
Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability
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）
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）
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）
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）
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）
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…
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）
Saratoga High School（萨拉托加高中）；BRAC University JPG School of Public Health（BRAC大学JPG公共卫生学院）
Diabetes is one of the most prevalent chronic diseases in Bangladesh, and as a result, Diabetic Retinopathy (DR) is widespread in the population. DR, an eye illness caused by diabetes, can lead to blindness if it is not identified and treated in its early stages. Unfortunately, diagnosis of DR requires medically trained professionals, but Bangladesh has limited specialists in comparison to its population. Moreover, the screening process is often expensive, prohibiting many from receiving timely and proper diagnosis. To address the problem, we introduce a deep learning algorithm which screens f…
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）
Managing patients with chronic diseases is a major and growing healthcare challenge in several countries. A chronic condition, such as diabetes, is an illness that lasts a long time and does not go away, and often leads to the patient's health gradually getting worse. While recent works involve raw electronic health record (EHR) from hospitals, this work uses only financial records from health plan providers (medical claims) to predict diabetes disease evolution with a self-attentive recurrent neural network. The use of financial data is due to the possibility of being an interface to internat…
Daffodil International University（达福德国际大学）；Florida Institute of Technology（佛罗里达理工学院）
In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complet…
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）
Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment
Institute of Diagnostic；Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany Chair for AI in Healthcare；Medicine, Technical University of Munich (TUM)；TUM University Hospital, Munich, Germany Department of Computing, Imperial College London, London, UK German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany
Institute for Basic Science (IBS)（基础科学研究院）；Seoul National University（首尔大学）；Pohang University of Science and Technology（浦项科技大学）
Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are tightly regulated by two counter-regulatory hormones, insulin and glucagon, and the failure of the glucose homeostasis leads to the common metabolic disease, diabetes mellitus. It is a chronic disease that has a long latent period the complicates detection of the disease at an early stage. The vast majority of diabetics result from that diminished effectiveness of…
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…
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）
Aging is a multidimensional process where phenotypes change at varying rates. Longitudinal studies of aging typically involve following a cohort of individuals over the course of several years. This design is hindered by cost, attrition, and subsequently small sample size. Alternative methodologies are therefore warranted. In this study, we used a variational autoencoder to estimate rates of aging from cross-sectional data from routine laboratory tests of 1.4 million individuals collected from 2016 to 2019. By incorporating metrics that would ensure model's stability and distinctness of the di…
AI-driven Multimodal Representation Learning for Latent Mediation Structure Discovery of Socioeconomic Disadvantage, Psychosocial Factors, and Cardiometabolic Multimorbidity: Insights from the All of Us Research Program
Yale University（耶鲁大学）
本研究基于All of Us研究计划数据，开发AI驱动的多模态中介框架，通过变分自编码器推导潜在表征，发现心理社会脆弱性是连接社会经济劣势与心脏代谢共病的关键中介通路，为探究健康相关复杂关系提供了新方法。
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…
The Affordable care Act of 2010 had introduced Readmission reduction program in 2012 to reduce avoidable re-admissions to control rising healthcare costs. Wound care impacts 15 of medicare beneficiaries making it one of the major contributors of medicare health care cost. Health plans have been exploring proactive health care services that can focus on preventing wound recurrences and re-admissions to control the wound care costs. With rising costs of Wound care industry, it has become of paramount importance to reduce wound recurrences & patient re-admissions. What factors are responsible for…
Learning Normal Representations for Blood Biomarkers
Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；Department of Systems Biology, Harvard Medical School（哈佛医学院系统生物学系）；Department of Medicine, Brigham and Women’s Hospital（布里洛妇产科医院医学系）；Department of Mathematics, Johns Hopkins University（约翰霍普金斯大学数学系）；Computational Health Informatics Program (CHIP), Boston Children’s Hospital（波士顿儿童医院计算健康信息学计划）；The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute（哈佛医学院伊万和弗rancesca伯克伍德家庭生活实验室合作项目及克劳斯研究机构）；Clalit Research Institute, Innovation Division, Clalit Health Services（克劳斯研究机构创新部门，克劳斯健康服务）；Faculty of Computer and Information Science, Ben Gurion University（本· Gurion大学计算机与信息科学系）
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）
