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 predicting the results. The problem selected for the study is the diagnosis of the Chronic Kidney Disease.The dataset used for the study consists of 400 instances and 24 attributes. The authors evaluated 12 classification techniques by applying them to the Chronic Kidney Disease data. In order to calculate efficiency, results of the prediction by candidate methods were compared with the actual medical results of the subject.The various metrics used for performance evaluation are predictive accuracy, precision, sensitivity and specificity. The results indicate that decision-tree performed best with nearly the accuracy of 98.6%, sensitivity of 0.9720, precision of 1 and specificity of 1.
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）
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…
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…
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…
Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability
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 …
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 …
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）
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）
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）
learning algorithms. In this paper, we review the classification algorithms used in the health care system (chronic diseases) and present the neural network-based Ensemble learning method. We briefly describe the commonly used algorithms and describe their critical properties. Materials and Methods: In this study, modern classification algorithms used in healthcare, examine the principles of these methods and guidelines, and to accurately diagnose and predict chronic diseases, superior machine learning algorithms with the neural network-based ensemble learning Is used. To do this, we use exper…
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）
Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors
School of Engineering & Technology, BML Munjal University（BML Munjal大学工程与技术学院）；Center for Advanced Data and Computational Science, BML Munjal University（BML Munjal大学先进数据与计算科学中心）
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…
Tecnologico de Monterrey（蒙特雷理工学院）；CHU Nancy（南锡大学中心医院）；Université de Lorraine（洛林大学）
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapses. However, currently, the associated ex-vivo diagnosis (known as morpho-constitutional analysis, MCA) is time-consuming, expensive, and requires a great deal of experience, as it requires a visual analysis component that is highly operator dependant. Recently, machine learning methods have been developed for in-vivo endoscopic stone recognition. Shallow methods have been demonstrated to be reliable and interpretable…
Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury
Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；Amsterdam Public Health（阿姆斯特丹公共卫生研究所）；Institute for Logic, Language and Computation（逻辑、语言与计算研究所）；Pacmed（Pacmed公司）
Faculty of Biomedical Engineering, Technion Institute of Technology（以色列理工学院 生物医学工程学院）；Faculty of Electrical Engineering, Technion Institute of Technology（以色列理工学院 电气工程学院）；Río Hortega University Hospital Valladolid（巴利亚多利德里奥奥尔塔加大学医院）；Biomedical Engineering Group, University of Valladolid（巴利亚多利德大学 生物医学工程组）；Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)（生物工程、生物材料与纳米医学网络生物医学研究中心（CIBER-BBN））
Objective: Chronic obstructive pulmonary disease (COPD) is a highly prevalent chronic condition. COPD is a major source of morbidity, mortality and healthcare costs. Spirometry is the gold standard test for a definitive diagnosis and severity grading of COPD. However, a large proportion of individuals with COPD are undiagnosed and untreated. Given the high prevalence of COPD and its clinical importance, it is critical to develop new algorithms to identify undiagnosed COPD, especially in specific groups at risk, such as those with sleep disorder breathing. To our knowledge, no research has look…
Georgia Institute of Technology（佐治亚理工学院）；James A. Haley Veterans’ Hospital（詹姆斯·A·哈利退伍军人医院）；University of Florida（佛罗里达大学）
Flow cytometry is a technique that measures multiple fluorescence and light scatter-associated parameters from individual cells as they flow a single file through an excitation light source. These cells are labeled with antibodies to detect various antigens and the fluorescence signals reflect antigen expression. Interpretation of the multiparameter flow cytometry data is laborious, time-consuming, and expensive. It involves manual interpretation of cell distribution and pattern recognition on two-dimensional plots by highly trained medical technologists and pathologists. Using various machine…
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））
Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death. This paper presents a Decision Support System, called DSSDDI, based on drug-drug interactions to support doctors prescribing decisions. DSSDDI contains three modules, Drug-Drug Interaction (DDI) module, Medical Decision (MD) module and Medical Support (MS) module. The DDI module learns safer and more effective drug representations from the drug-drug int…
National Tsing Hua University（国立清华大学）；Massachusetts Institute of Technology（麻省理工学院）；Emory University（埃默里大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Emory University School of Medicine（埃默里大学医学院）
Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world and the third leading cause of mortality worldwide. It is often underdiagnosed or not diagnosed until later in the disease course. Spirometry tests are the gold standard for diagnosing COPD but can be difficult to obtain, especially in resource-poor countries. Chest X-rays (CXRs), however, are readily available and may serve as a screening tool to identify patients with COPD who should undergo further testing. Currently, no research applies deep learning (DL) algorithms that use large multi-si…
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）
Dalian University of Technology（大连理工大学）；Worcester Polytechnic Institute（伍斯特理工学院）；Indiana University（印第安纳大学）；Alibaba DAMO Academy（阿里巴巴达摩院）
Objective: Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles. This paper proposes leveraging online shopping behaviors as a proxy for personal lifestyle choices to improve chronic disease prevention literacy, targeted for times when e-commerce user experience has been assimilated into most people's everyday lives. Methods: Longitudinal query logs and purchase records from 15 million online shoppers were accessed, constructing a broad spectrum…
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）
On the in vivo recognition of kidney stones using machine learning
Tecnologico de Monterrey（蒙特雷理工学院）；Université de Lorraine（洛林大学）；CHRU Pellegrin（佩勒兰大学地区医疗中心）；IADI-UL-INSERM（洛林大学-法国国家健康与医学研究院先进诊断与工业应用研究所）；CHRU Nancy（南锡大学地区医疗中心）
Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence
Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost. Clinical diagnostic data are often highly imbalanced; therefore, we aim to maximize the $F_1$ score instead of the error rate. However, optimizing the non-concave $F_1$ score is not a classic RL problem, thus invalidates standard RL methods. To remedy this issue, we develop a reward shaping approach, leveraging properties of the …
University of Georgia（佐治亚大学）；Quincy University（昆西大学）；Mayo Clinic（梅奥诊所）；University of Pittsburgh（匹兹堡大学）
Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide, where knee OA takes more than 80% of commonly affected joints. Knee OA is not a curable disease yet, and it affects large columns of patients, making it costly to patients and healthcare systems. Etiology, diagnosis, and treatment of knee OA might be argued by variability in its clinical and physical manifestations. Although knee OA carries a list of well-known terminology aiming to standardize the nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes of the chronic joint disease, in practice there …
Parkinson's Disease (PD) is a chronic, degenerative disorder which leads to a range of motor and cognitive symptoms. PD diagnosis is a challenging task since its symptoms are very similar to other diseases such as normal ageing and essential tremor. Much research has been applied to diagnosing this disease. This project aims to automate the PD diagnosis process using deep learning, Recursive Neural Networks (RNN) and Convolutional Neural Networks (CNN), to differentiate between healthy and PD patients. Besides that, since different datasets may capture different aspects of this disease, this p…
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）
Obstructive sleep apnea is a serious condition causing a litany of health problems especially in the pediatric population. However, this chronic condition can be treated if diagnosis is possible. The gold standard for diagnosis is an overnight sleep study, which is often unobtainable by many potentially suffering from this condition. Hence, we attempt to develop a fast non-invasive diagnostic tool by training a classifier on 2D and 3D facial images of a patient to recognize facial features associated with obstructive sleep apnea. In this comparative study, we consider both persistent homology …
