University of Lugano（卢加诺大学）；Sapienza University of Rome（罗马大学）；Technical University of Munich（慕尼黑工业大学）；Ludwig Maximilians Universität München（慕尼黑大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Imperial College London（帝国理工学院）；Twitter（推特）
Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein
发表信息：
In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 643-653. Springer, Cham, 2020
Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer-Aided Diagnosis (CADx) and disease prediction. A key component in these models is to build a population graph, where the graph adjacency matrix represents pair-wise patient similarities. Until now, the similarity metrics have been defined manually, usually based on meta-features like demographics or clinical scores. The definition of the metric, however, needs careful tuning, as GCNs are very sensitive to the graph structure. In this paper, we demonstrate for the first time in the CADx domain that it is possible to learn a single, optimal graph towards the GCN's downstream task of disease classification. To this end, we propose a novel, end-to-end trainable graph learning architecture for dynamic and localized graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is spatial and inductive, and can thus infer previously unseen patients as well. We demonstrate significant classification improvements with our learned graph on two CADx problems in medicine. We further explain and visualize this result using an artificial dataset, underlining the importance of graph learning for more accurate and robust inference with GCNs in medical applications.
Learning Disease-Sensitive Latent Interaction Graphs From Noisy Cardiac Flow Measurements
Department of Computer Science, University of Bath（巴斯大学计算机科学系）；Department of Mechanical Engineering, University of Bath（巴斯大学机械工程系）；Christian Doppler Laboratory for Mechanical Circulatory Support, Department of Cardiac and Thoracic Aortic Surgery, Medical University of Vienna（维也纳医学大学心脏和胸主动脉外科系基督教多普勒实验室）
Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes
University of Zurich（苏黎世大学）；ETH AI Center（苏黎世联邦理工学院人工智能中心）；Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；University Hospital Zurich（苏黎世大学医院）；Bogomolets National Medical University（博戈莫列茨国立医科大学）
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…
MIRACLE: Multi-task Learning based Interpretable Regulation of Autoimmune Diseases through Common Latent Epigenetics
University of Illinois Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Michigan-Shanghai Jiao Tong University Joint Institute（上海交通大学密歇根学院）；Shanghai Jiao Tong University（上海交通大学）；University of Michigan（密歇根大学）
Identifying latent disease factors differently expressed in patient subgroups using group factor analysis
University College London（伦敦大学学院）；Max Planck University College London Centre for Computational Psychiatry and Ageing Research（马克斯·普朗克伦敦大学学院计算精神病学与衰老研究中心）；Wellcome Centre for Human Neuroimaging（惠康人类神经影像中心）；UCL Queen Square Institute of Neurology（伦敦大学学院女王广场神经病学研究所）；Erasmus Medical Centre（伊拉斯姆斯医学中心）；Hospital Clinic（临床医院）
IBM Hellas（IBM希腊公司）；National Technical University of Athens（雅典国立技术大学）；University of Lincoln（林肯大学）
This paper presents a new method for medical diagnosis of neurodegenerative diseases, such as Parkinson's, by extracting and using latent information from trained Deep convolutional, or convolutional-recurrent Neural Networks (DNNs). In particular, our approach adopts a combination of transfer learning, k-means clustering and k-Nearest Neighbour classification of deep neural network learned representations to provide enriched prediction of the disease based on MRI and/or DaT Scan data. A new loss function is introduced and used in the training of the DNNs, so as to perform adaptation of the ge…
Electronic health records (EHR) are rich heterogeneous collection of patient health information, whose broad adoption provides great opportunities for systematic health data mining. However, heterogeneous EHR data types and biased ascertainment impose computational challenges. Here, we present mixEHR, an unsupervised generative model integrating collaborative filtering and latent topic models, which jointly models the discrete distributions of data observation bias and actual data using latent disease-topic distributions. We apply mixEHR on 12.8 million phenotypic observations from the MIMIC d…
A Structural FHMM for Interpretable Disease Trajectories in T2DM
Swiss Data Science Center (SDSC)（瑞士数据科学中心（SDSC））；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；ETH Zürich（苏黎世联邦理工学院）；Institute of Pharmaceutical Sciences（药学院）
RETINA-AI Health, Inc.（RETINA-AI健康公司）；MD Anderson Cancer Center（MD安德森癌症中心）
In this paper, we seek a clinically-relevant latent code for representing the spectrum of macular disease. Towards this end, we construct retina-VAE, a variational autoencoder-based model that accepts a patient profile vector (pVec) as input. The pVec components include clinical exam findings and demographic information. We evaluate the model on a subspectrum of the retinal maculopathies, in particular, exudative age-related macular degeneration, central serous chorioretinopathy, and polypoidal choroidal vasculopathy. For these three maculopathies, a database of 3000 6-dimensional pVecs (1000 …
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…
Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis
University of Zurich（苏黎世大学）；Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；University Hospital Zurich（苏黎世大学医院）；Bogomolets National Medical University（博格莫列茨国立医科大学）；CHU Lille（里尔大学医院中心）；Lille University（里尔大学）；University of Brescia（布雷西亚大学）；Charles University（查理大学）；University College London（伦敦大学学院）；University of Split（斯普利特大学）；University Hospital Center Split（斯普利特大学医院中心）
With the wide application of electronic health records (EHR) in healthcare facilities, health event prediction with deep learning has gained more and more attention. A common feature of EHR data used for deep-learning-based predictions is historical diagnoses. Existing work mainly regards a diagnosis as an independent disease and does not consider clinical relations among diseases in a visit. Many machine learning approaches assume disease representations are static in different visits of a patient. However, in real practice, multiple diseases that are frequently diagnosed at the same time ref…
Multiple Sclerosis (MS) and microvascular leukoencephalopathy are two distinct neurological conditions, the first caused by focal autoimmune inflammation in the central nervous system, the second caused by chronic white matter damage from atherosclerotic microvascular disease. Both conditions lead to signal anomalies on Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images, which can be distinguished by an expert neuroradiologist, but which can look very similar to the untrained eye as well as in the early stage of both diseases. In this paper, we attempt to train a 3-dime…
While many machine learning methods have been used for medical prediction and risk factor analysis on healthcare data, most prior research has involved single-task learning (STL) methods. However, healthcare research often involves multiple related tasks. For instance, implementation of disease scores prediction and risk factor analysis in multiple subgroups of patients simultaneously and risk factor analysis at multi-levels synchronously. In this paper, we developed a new ensemble machine learning Python package based on multi-task learning (MTL), referred to as the Med-Multi-Task Learning (M…
Black Sigatoka disease severely decreases global banana production, and climate change aggravates the problem by altering fungal species distributions. Due to the heavy financial burden of managing this infectious disease, farmers in developing countries face significant banana crop losses. Though scientists have produced mathematical models of infectious diseases, adapting these models to incorporate climate effects is difficult. We present MR. NODE (Multiple predictoR Neural ODE), a neural network that models the dynamics of black Sigatoka infection learnt directly from data via Neural Ordin…
McGill University（麦吉尔大学）；McGill Adult Unit for Congenital Heart Disease Excellence（麦吉尔大学成人先天性心脏病卓越中心）
Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled. We present MixEHR-S to jointly infer specialist-disease topics from the EHR data. As the key contribution, we model the specialist assignments and ICD-coded diagnoses as the latent topics based on patient's underlying disease topic mixture in a novel unified supervised hierarchical Bayesian topic model. For efficient inference, we developed a…
Indian Institute of Technology Bhubaneswar（布巴内斯瓦尔印度理工学院）；G.G Vishwavidyalaya, Central University（G.G大学（中央大学））
Parkinson's disease (PD) is one of the major public health problems in the world. It is a well-known fact that around one million people suffer from Parkinson's disease in the United States whereas the number of people suffering from Parkinson's disease worldwide is around 5 million. Thus, it is important to predict Parkinson's disease in early stages so that early plan for the necessary treatment can be made. People are mostly familiar with the motor symptoms of Parkinson's disease, however, an increasing amount of research is being done to predict the Parkinson's disease from non-motor sympt…
Cardiovascular diseases (CVDs) are one of the most common chronic illnesses that affect peoples health. Early detection of CVDs can reduce mortality rates by preventing or reducing the severity of the disease. Machine learning algorithms are a promising method for identifying risk factors. This paper proposes a proposed recursive feature elimination-based gradient boosting (RFE-GB) algorithm in order to obtain accurate heart disease prediction. The patients health record with important CVD features has been analyzed for the evaluation of the results. Several other machine learning methods were…
Drug repositioning is designed to discover new uses of known drugs, which is an important and efficient method of drug discovery. Researchers only use one certain type of Collaborative Filtering (CF) models for drug repositioning currently, like the neighborhood based approaches which are good at mining the local information contained in few strong drug-disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug-disease associations. Few researchers have combined these two types of CF models to derive a hybrid model …
Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model
Department of Computer Science Emory University Atlanta GA USA（埃默里大学计算机科学系）；Center for Data Science Nell Hodgson Woodruff School of Nursing Emory University Atlanta GA USA（埃默里大学数据科学中心）；Department of Computer Science Georgia State University Atlanta GA USA（佐治亚州立大学计算机科学系）；Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Atlanta GA USA（神经影像与数据科学转化研究三机构中心（TReNDS））
Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction
College of Informatics, Huazhong Agricultural University（华中农业大学信息学院）；Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University（华中农业大学农业生物信息学湖北省重点实验室）
PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection
National Institute of Health Data Science, Peking University, China（北京大学国家健康数据科学研究院）；School of Intelligence Science and Technology, Peking University, China（北京大学智能科学与技术学院）；Data Science Institute, Imperial College London, UK（伦敦帝国理工学院数据科学研究院）；University of Chinese Academy of Sciences, China（中国科学院大学）
Disentanglement of Biological and Technical Factors via Latent Space Rotation in Clinical Imaging Improves Disease Pattern Discovery
Computational Imaging Research Lab, Department for Biomedical Imaging and Image-guided Therapy（生物医学成像与影像引导治疗部门计算成像研究实验室）；Comprehensive Center for Artificial Intelligence in Medicine（医学人工智能综合中心）；Division of General and Pediatric Radiology, Department for Biomedical Imaging and Image-guided Therapy（普通放射科与儿科放射科，生物医学成像与影像引导治疗部门）；Medical University of Vienna（维也纳医学大学）
McGovern Institute for Brain Research, Massachusetts Institute of Technology（麻省理工学院麦戈文脑研究所）；Complutense University of Madrid（马德里康普顿斯大学）；Harvard Medical School（哈佛医学院）
Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G), which can learn highly informative network features by mapping high-dimensional resting-state brain networks into a low-dimensional latent space. These latent distribution-based embeddings enable a quantitative characterization of subtle and heterogeneous brain connectivit…
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学院）；Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属同济医院放射科）；Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院放射科）；Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司研发部）；Department of Radiology, China-Japan Union Hospital of Jilin University（吉林大学中日联谊医院放射科）
Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describ…
Cryptogenic stroke and migraine: using probabilistic independence and machine learning to uncover latent sources of disease from the electronic health record
Vanderbilt University School of Medicine（范德比尔特大学医学院）；Vanderbilt University Medical Center（范德比尔特大学医学中心）
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 …
An explainable framework for the relationship between dementia and glucose metabolism patterns
Department of Signal Processing and Biomedical Applications, University of Granada（信号处理与生物医学应用系，格拉纳达大学）；Department of Statistics and Operations Research, University of Valencia（统计与运筹学系，瓦伦西亚大学）；Department of Communication Engineering, University of Malaga（通信工程系，马拉加大学）
Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for directly training deep learning models on individual disease data or all the data from different diseases. Mortality prediction for these patients with different diseases can be viewed as a multi-task learning problem with insufficient data and large task number. But the tasks with little training data also make it hard to train task-specific modules in multi-task learning m…
Disease-gene prediction (DGP) refers to the computational challenge of predicting associations between genes and diseases. Effective solutions to the DGP problem have the potential to accelerate the therapeutic development pipeline at early stages via efficient prioritization of candidate genes for various diseases. In this work, we introduce the variational graph auto-encoder (VGAE) as a promising unsupervised approach for learning powerful latent embeddings in disease-gene networks that can be used for the DGP problem, the first approach using a generative model involving graph neural networ…
Oulu University（奥卢大学）；University of Florida（佛罗里达大学）
Millions of patients suffer from rare diseases around the world. However, the samples of rare diseases are much smaller than those of common diseases. In addition, due to the sensitivity of medical data, hospitals are usually reluctant to share patient information for data fusion citing privacy concerns. These challenges make it difficult for traditional AI models to extract rare disease features for the purpose of disease prediction. In this paper, we overcome this limitation by proposing a novel approach for rare disease prediction based on federated meta-learning. To improve the prediction …
Identification of disease genes, which are a set of genes associated with a disease, plays an important role in understanding and curing diseases. In this paper, we present a biomedical knowledge graph designed specifically for this problem, propose a novel machine learning method that identifies disease genes on such graphs by leveraging recent advances in network biology and graph representation learning, study the effects of various relation types on prediction performance, and empirically demonstrate that our algorithms outperform its closest state-of-the-art competitor in disease gene ide…
City College and the Graduate Center of CUNY（纽约市立大学城市学院与研究生中心）；UniDT
In this paper, we propose a maximum entropy method for predicting disease risks. It is based on a patient's medical history with diseases coded in ICD-10 which can be used in various cases. The complete algorithm with strict mathematical derivation is given. We also present experimental results on a medical dataset, demonstrating that our method performs well in predicting future disease risks and achieves an accuracy rate twice that of the traditional method. We also perform a comorbidity analysis to reveal the intrinsic relation of diseases.
Time-dependent Probabilistic Generative Models for Disease Progression
BCAM-Basque Center for Applied Mathematics（巴斯克应用数学中心）；Hospital Universitario Cruces（克鲁塞斯大学医院）；University of the Basque Country UPV/EHU（巴斯克大学）；Osakidetza（奥萨基德萨）
Cloud Computing Research Center（云计算研究中心）；National Sun Yat-sen University（国立中山大学）；National Institute of Technology（国家理工学院）
In this modern era of overpopulation disease prediction is a crucial step in diagnosing various diseases at an early stage. With the advancement of various machine learning algorithms, the prediction has become quite easy. However, the complex and the selection of an optimal machine learning technique for the given dataset greatly affects the accuracy of the model. A large amount of datasets exists globally but there is no effective use of it due to its unstructured format. Hence, a lot of different techniques are available to extract something useful for the real world to implement. Therefore…
Seoul National University（首尔国立大学）；Samsung Electronics Co. Ltd.（三星电子有限公司）；Seoul National University College of Medicine（首尔国立大学医学院）
Electronic health records (EHRs) have contributed to the computerization of patient records and can thus be used not only for efficient and systematic medical services, but also for research on biomedical data science. However, there are many missing values in EHRs when provided in matrix form, which is an important issue in many biomedical EHR applications. In this paper, we propose a two-stage framework that includes missing data imputation and disease prediction to address the missing data problem in EHRs. We compared the disease prediction performance of generative adversarial networks (GA…
Objective: To combine medical knowledge and medical data to interpretably predict the risk of disease. Methods: We formulated the disease prediction task as a random walk along a knowledge graph (KG). Specifically, we build a KG to record relationships between diseases and risk factors according to validated medical knowledge. Then, a mathematical object walks along the KG. It starts walking at a patient entity, which connects the KG based on the patient current diseases or risk factors and stops at a disease entity, which represents the predicted disease. The trajectory generated by the objec…
College of Computing and Information Technology, University of Bisha（比沙大学计算与信息技术学院）
Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwat…
Coronary heart disease, which is a form of cardiovascular disease (CVD), is the leading cause of death worldwide. The odds of survival are good if it is found or diagnosed early. The current report discusses a comparative approach to the classification of coronary heart disease datasets using machine learning (ML) algorithms. The current study created and tested several machine-learning-based classification models. The dataset was subjected to Smote to handle unbalanced classes and feature selection technique in order to assess the impact on two distinct performance metrics. The results show t…
Technische Universität München（慕尼黑工业大学）；Klinikum der Universität München（慕尼黑大学附属医院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Structural data from Electronic Health Records as complementary information to imaging data for disease prediction. We incorporate novel weighting layer into the Graph Convolutional Networks, which weights every element of structural data by exploring its relation to the underlying disease. We demonstrate the superiority of our developed technique in terms of computational speed and obtained encouraging results where our method outperforms the state-of-the-art methods when applied to two publicly available datasets ABIDE and Chest X-ray in terms of relative performance for the accuracy of pred…
Technical University of Munich（慕尼黑工业大学）；Sharif University of Technology（谢里夫理工大学）；National Institute of Technology Tiruchirappalli（蒂鲁吉拉伯利国家理工学院）；Ludwig Maximilians Universität München（路德维希-马克西米利安慕尼黑大学）；Klinikum der Universität München（慕尼黑大学医院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks (GCNs) in particular have been explored on a wide variety of problems such as disease prediction, segmentation, and matrix completion by leveraging large, multimodal datasets. In this paper, we introduce a new spectral domain architecture for deep learning on graphs for disease prediction. The novelty lies in defining geometric 'inception modules' which are capable of capturing intra- and inter-graph structural heteroge…
