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））
Tian Bian, Yuli Jiang, Jia Li, Tingyang Xu, Yu Rong, Yi Su, Timothy Kwok, Helen Meng, Hong Cheng
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 interactions. To capture the potential causal relationship between DDI and medication use, the MD module considers the representations of patients and drugs as context, DDI and patients' similarity as treatment, and medication use as outcome to construct counterfactual links for the representation learning. Furthermore, the MS module provides drug candidates to doctors with explanations. Experiments on the chronic data collected from the Hong Kong Chronic Disease Study Project and a public diagnostic data MIMIC-III demonstrate that DSSDDI can be a reliable reference for doctors in terms of safety and efficiency of clinical diagnosis, with significant improvements compared to baseline methods.
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…
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）
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）
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 …
Universidade Estadual de Maringá (UEM)（马林加州立大学）；Universidade Federal do Paraná (UFPR)（巴拉那联邦大学）；Instituto Federal do Paraná (IFPR)（巴拉那联邦理工学院）
Studies recently accomplished on the Enteric Nervous System have shown that chronic degenerative diseases affect the Enteric Glial Cells (EGC) and, thus, the development of recognition methods able to identify whether or not the EGC are affected by these type of diseases may be helpful in its diagnoses. In this work, we propose the use of pattern recognition and machine learning techniques to evaluate if a given animal EGC image was obtained from a healthy individual or one affect by a chronic degenerative disease. In the proposed approach, we have performed the classification task with handcr…
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）
Computational drug repositioning aims to discover new therapeutic diseases for marketed drugs and has the advantages of low cost, short development cycle, and high controllability compared to traditional drug development. The matrix factorization model has become the cornerstone technique for computational drug repositioning due to its ease of implementation and excellent scalability. However, the matrix factorization model uses the inner product to represent the association between drugs and diseases, which is lacking in expressive ability. Moreover, the degree of similarity of drugs or disea…
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）
KU Leuven（鲁汶大学）；Open University of the Netherlands（荷兰开放大学）；Radboud University Nijmegen（拉德堡德大学奈梅亨分校）
Managing patients with multimorbidity often results in polypharmacy: the prescription of multiple drugs. However, the long-term effects of specific combinations of drugs and diseases are typically unknown. In particular, drugs prescribed for one condition may result in adverse effects for the other. To investigate which types of drugs may affect the further progression of multimorbidity, we query models of diseases and prescriptions that are learned from primary care data. State-of-the-art tractable Bayesian network representations, on which such complex queries can be computed efficiently, ar…
Recent years have seen an increased focus into the tasks of predicting hospital inpatient risk of deterioration and trajectory evolution due to the availability of electronic patient data. A common approach to these problems involves clustering patients time-series information such as vital sign observations) to determine dissimilar subgroups of the patient population. Most clustering methods assume time-invariance of vital-signs and are unable to provide interpretability in clusters that is clinically relevant, for instance, event or outcome information. In this work, we evaluate three differ…
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…
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 Obstructive Pulmonary Disease (COPD) is an irreversible airway obstruction with a high societal burden. Although smoking is known to be the biggest risk factor, additional components need to be considered. In this study, we aim to identify COPD risk factors by applying machine learning models that integrate sociodemographic, clinical, and genetic data to predict COPD development.
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）
Objectives-Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients (age 65 years and above) functional ability, physical health, and cognitive wellbeing. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases. Materials and…
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…
Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability
Boston Medical Center（波士顿医学中心）；Boston University（波士顿大学）
Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic disease, heart disease and diabetes, and develop data-driven methods to predict hospitalizations due to these conditions. We base these predictions on the patients' medical history, recent and more distant, as described in their Electronic Health Records (EHR). We formulate the prediction problem as a binary classification problem and consider a variety of machine learning methods, including kernelized and sparse Support…
Arizona State University（亚利桑那州立大学）；Univar Solutions Inc.（Univar Solutions公司）
Chronic diseases such as diabetes are quite prevalent in the world and are responsible for a significant number of deaths per year. In addition, treatments for such chronic diseases account for a high healthcare cost. However, research has shown that diabetes can be proactively managed and prevented while lowering these healthcare costs. We have mined a sample of ten million customers' 360-degree data representing the state of Texas, USA, with attributes current as of late 2018. The sample received from a market research data vendor has over 1000 customer attributes consisting of demography, l…
BiBLDR: Bidirectional Behavior Learning for Drug Repositioning
School of Computer Science, Hunan First Normal University（湖南第一师范学院计算机科学学院）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）
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…
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 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 …
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）
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…
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…
DrugGen 2: A disease-aware language model for enhancing drug discovery
Regenerative Medicine Research Center（再生医学研究中心）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；Isfahan Neuroscience Research Center（伊斯法罕神经科学研究中心）；School of Pharmacy（药学院）；Department of Medicinal Chemistry（药理化学系）；Cardiovascular Research Institute（心血管研究学院）；Department of Genetics and Molecular Biology（基因与分子生物学系）；Bioinformatics Research Center（生物信息学研究中心）
Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence
In this work we present a deep learning approach to conduct hypothesis-free, transcriptomics-based matching of drugs for diseases. Our proposed neural network architecture is trained on approved drug-disease indications, taking as input the relevant disease and drug differential gene expression profiles, and learns to identify novel indications. We assemble an evaluation dataset of disease-drug indications spanning 68 diseases and evaluate in silico our approach against the most widely used transcriptomics-based matching baselines, CMap and the Characteristic Direction. Our results show a more…
Computational Approaches for Predicting Drug-Disease Associations: A Comprehensive Review
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿研究院）
