Longitudinal modeling of MS patient trajectories improves predictions of disability progression
KU Leuven（鲁汶大学）；Hasselt University（哈瑟尔特大学）；Charles University in Prague（布拉格查理大学）；General University Hospital（综合大学医院）；University of Bari（巴里大学）；Hospital Universitario Virgen Macarena（马卡雷纳圣母大学医院）；Dokuz Eylul University（多库兹埃卢尔大学）；University G. d’Annunzio（邓南遮大学）；University Hospital Basel（巴塞尔大学医院）；University of Basel（巴塞尔大学）
Edward De Brouwer, Thijs Becker, Yves Moreau, Eva Kubala Havrdova, Maria Trojano, Sara Eichau, Serkan Ozakbas, Marco Onofrj, Pierre Grammond, Jens Kuhle, Ludwig… 展开作者
Edward De Brouwer, Thijs Becker, Yves Moreau, Eva Kubala Havrdova, Maria Trojano, Sara Eichau, Serkan Ozakbas, Marco Onofrj, Pierre Grammond, Jens Kuhle, Ludwig Kappos, Patrizia Sola, Elisabetta Cartechini, Jeannette Lechner-Scott, Raed Alroughani, Oliver Gerlach, Tomas Kalincik, Franco Granella, Francois GrandMaison, Roberto Bergamaschi, Maria Jose Sa, Bart Van Wijmeersch, Aysun Soysal, Jose Luis Sanchez-Menoyo, Claudio Solaro, Cavit Boz, Gerardo Iuliano, Katherine Buzzard, Eduardo Aguera-Morales, Murat Terzi, Tamara Castillo Trivio, Daniele Spitaleri, Vincent Van Pesch, Vahid Shaygannej, Fraser Moore, Celia Oreja Guevara, Davide Maimone, Riadh Gouider, Tunde Csepany, Cristina Ramo-Tello, Liesbet Peeters
Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work, we address the task of optimally extracting information from longitudinal patient data in the real-world setting with a special focus on the sporadic sampling problem. Using the MSBase registry, we show that with machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization, we can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.86, which represents a 33% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction.
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression
LaTIM UMR 1101；Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；IMT Atlantique（大西洋电信学院）；Lariboisière Hospital（拉里布瓦西耶尔医院）；AP-HP（巴黎公共医疗援助局）；CHRU Brest（布雷斯特大学医院中心）
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs
Medical University of Vienna（维也纳医科大学）；BioMedIA, Imperial College London（伦敦帝国理工学院生物医学图像分析研究所）；Institute for AI and Informatics in Medicine, Klinikum rechts der Isar, Technical University Munich（慕尼黑工业大学伊萨尔河畔医院医学人工智能与信息学研究所）；Department of Ophthalmology, University of Basel（巴塞尔大学眼科系）
TrajSurv: Learning Continuous Latent Trajectories from Electronic Health Records for Trustworthy Survival Prediction
Department of Biomedical Informatics and Medical Education University of Washington（生物医学信息学与医学教育系，华盛顿大学）；Fred Hutch Cancer Center（Fred Hutch癌症中心）；Department of Biomedical Informatics Harvard University（生物医学信息学系，哈佛大学）
Detection of diabetic retinopathy using longitudinal self-supervised learning
LaTIM UMR 1101, Inserm（LaTIM UMR 1101 法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；IMT Atlantique（大西洋电信学院）；Lariboisière Hospital, AP-HP（拉里布瓦西耶医院（巴黎公共医疗集团））；CHRU Brest（布雷斯特大学医疗中心）
Google Research（谷歌研究院）；DeepMind（深度思维）；Duke University Health System（杜克大学卫生系统）
Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for disease identification. However, studies have shown that these modalities are not consistent with disease activity such as symptoms or disease progression. Furthermore, the cost of collecting data from these modalities is high, leading to scarce evaluations. In this work, we used multi-dimensional, affordable, physical and smartphone-based performance outcome measures (POM) in conjunction with demographic data to predic…
Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT
Medical University of Vienna（维也纳医科大学）；Imperial College London（伦敦帝国学院）；Technical University Munich（慕尼黑工业大学）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔兹眼科医院NHS基金会信托）；University of Basel（巴塞尔大学）；University of Southampton（南安普顿大学）
Recent contrastive learning methods achieved state-of-the-art in low label regimes. However, the training requires large batch sizes and heavy augmentations to create multiple views of an image. With non-contrastive methods, the negatives are implicitly incorporated in the loss, allowing different images and modalities as pairs. Although the meta-information (i.e., age, sex) in medical imaging is abundant, the annotations are noisy and prone to class imbalance. In this work, we exploited already existing temporal information (different visits from a patient) in a longitudinal optical coherence…
IQIVA Inc.（艾昆纬公司）；University of Minnesota（明尼苏达大学）；University of Chicago（芝加哥大学）
Models have been proposed to extract temporal patterns from longitudinal electronic health records (EHR) for clinical predictive models. However, the common relations among patients (e.g., receiving the same medical treatments) were rarely considered. In this paper, we propose to learn patient similarity features as phenotypes from the aggregated patient-medical service matrix using non-negative matrix factorization. On real-world medical claim data, we show that the learned phenotypes are coherent within each group, and also explanatory and indicative of targeted diseases. We conducted experi…
Weizmann Institute of Science（魏茨曼科学研究所）；Tel Aviv University（特拉维夫大学）；DKFZ（德国癌症研究中心）
Precision medicine is a clinical approach for disease prevention, detection and treatment, which considers each individual's genetic background, environment and lifestyle. The development of this tailored avenue has been driven by the increased availability of omics methods, large cohorts of temporal samples, and their integration with clinical data. Despite the immense progression, existing computational methods for data analysis fail to provide appropriate solutions for this complex, high-dimensional and longitudinal data. In this work we have developed a new method termed TCAM, a dimensiona…
IBM Research（IBM研究院）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；JDRF；University of Washington（华盛顿大学）；Lund University（隆德大学）；University of Oulu（奥卢大学）；University of Colorado（科罗拉多大学）
Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods. We apply it to the domain of Type 1 Diabetes (T1D) using large longitudinal observational data from the T1DI study group. Our method discovers distinct disease progression trajectories that corrob…
A Longitudinal Attribute-Conditioned Neural Network for Modeling Health-State Transition Probabilities in Temporally Irregular Data: The LANTERN Framework
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；School of Life Sciences, Arizona State University（亚利桑那州立大学生命科学学院）；School of Mathematical and Statistical Sciences, Arizona State University（亚利桑那州立大学数学与统计科学学院）
Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting
Emory University（埃默里大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Illinois Institute of Technology（伊利诺伊理工学院）；Lehigh University（莱斯大学）；University of Pittsburgh（匹兹堡大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
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））
Modeling disease progression in healthcare administrative databases is complicated by the fact that patients are observed only at irregular intervals when they seek healthcare services. In a longitudinal cohort of 76,888 patients with chronic obstructive pulmonary disease (COPD), we used a continuous-time hidden Markov model with a generalized linear model to model healthcare utilization events. We found that the fitted model provides interpretable results suitable for summarization and hypothesis generation.
University of Cambridge（剑桥大学）；University of Southampton（南安普顿大学）
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics could lead to more timely treatment. The primary objective of this study is to explore the potential of longitudinal audio sam…
A Machine Learning-Based Framework for Discovering Huntington's Disease Stages: Integrating Graph Representation Learning and clustering to Uncover Progression Dynamics in Longitudinal Enroll-HD Dataset
School of Mathematical and Computer Sciences, Heriot-Watt University（赫瑞斯-沃德大学数学与计算机科学学院）
University of Liverpool（利物浦大学）；Royal Liverpool University Hospital（皇家利物浦大学医院）
Prognostic models aim to predict the future course of a disease or condition and are a vital component of personalized medicine. Statistical models make use of longitudinal data to capture the temporal aspect of disease progression; however, these models require prior feature extraction. Deep learning avoids explicit feature extraction, meaning we can develop models for images where features are either unknown or impossible to quantify accurately. Previous prognostic models using deep learning with imaging data require annotation during training or only utilize a single time point. We propose …
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；National Institutes of Health（美国国立卫生研究院）
Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, personalized care and treatment planning, as well as patient counseling about their individual progression is limited. Recent developments in machine learning to detect hidden patterns in complex, multi-dimensional datasets provides significant opportunities to address this criti…
School of Computing Science, Newcastle University（纽卡斯尔大学计算科学学院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Merck（默克公司）；Bioscience Institute, Newcastle University（纽卡斯尔大学生物科学研究所）；Institut de Recherches Internationales Servier（赛诺菲国际研究所）；GlaxoSmithKline（葛兰素史克公司）；Delft University of Technology（代尔夫特理工大学）；State Research Institute Centre for Innovative Medicine（创新医学国家研究中心）；University of Oulu（奥卢大学）；Versus Arthritis（对抗关节炎组织）
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class class…
In clinical practice and biomedical research, measurements are often collected sparsely and irregularly in time while the data acquisition is expensive and inconvenient. Examples include measurements of spine bone mineral density, cancer growth through mammography or biopsy, a progression of defective vision, or assessment of gait in patients with neurological disorders. Since the data collection is often costly and inconvenient, estimation of progression from sparse observations is of great interest for practitioners. From the statistical standpoint, such data is often analyzed in the context…
ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images
Yale University（耶鲁大学）；University of California, San Francisco（加州大学旧金山分校）；Mila - Quebec AI Institute（魁北克AI研究所）；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；Northern California Retina Vitreous Associates（北加州视网膜与玻璃体协会）
Modeling Parkinson's Disease Progression Using Longitudinal Voice Biomarkers: A Comparative Study of Statistical and Neural Mixed-Effects Models
Department of Mathematical Sciences（数学科学系）；Naveen Jindal School of Management（奈文·金达管理学院）；Department of Biology（生物系）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Duke University（杜克大学）；The Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院第二附属医院）；Department of Neurosurgery（神经外科系）
MRExtrap: Longitudinal Aging of Brain MRIs using Linear Modeling in Latent Space
University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Cluster of Excellence: Machine Learning – New Perspectives for Science（卓越中心：机器学习——科学的新视角）；University of Lucerne（卢塞恩大学）
Robust forecasting of the future anatomical changes inflicted by an ongoing disease is an extremely challenging task that is out of grasp even for experienced healthcare professionals. Such a capability, however, is of great importance since it can improve patient management by providing information on the speed of disease progression already at the admission stage, or it can enrich the clinical trials with fast progressors and avoid the need for control arms by the means of digital twins. In this work, we develop a deep learning method that models the evolution of age-related disease by proce…
Diagnostic-Guided Longitudinal Modeling for Forecasting Retinal Atrophy Progression
Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School（眼科部门，麻省眼耳医院，哈佛医学院）；Harvard Ophthalmology AI Lab, Schepens Eye Research Institute, Harvard Medical School（哈佛眼科人工智能实验室，斯科普斯眼研究所，哈佛医学院）；Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School（眼科基因组研究所，麻省眼耳医院，哈佛医学院）；Broad Institute of MIT and Harvard, Cambridge, MA, USA（麻省理工学院与哈佛大学 Broad 实验室，剑桥，马萨诸塞州，美国）
A particular challenge for disease progression modeling is the heterogeneity of a disease and its manifestations in the patients. Existing approaches often assume the presence of a single disease progression characteristics which is unlikely for neurodegenerative disorders such as Parkinson's disease. In this paper, we propose a hierarchical time-series model that can discover multiple disease progression dynamics. The proposed model is an extension of an input-output hidden Markov model that takes into account the clinical assessments of patients' health status and prescribed medications. We …
A Deep Registration Method for Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis
Graduate School of Health Sciences, Hokkaido University（北海道大学健康科学研究院）；Research Center For Integrated Quantum Electronics, Hokkaido University（北海道大学综合量子电子学研究中心）；Graduate School of Information Science and Technology, Hokkaido University（北海道大学信息科学与技术研究院）；Faculty of Health Sciences, Hokkaido University（北海道大学健康科学学院）
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCT
Medical University of Vienna（维也纳医科大学）；University of Basel（巴塞尔大学）；University of Michigan（密歇根大学）；NIHR Moorfields Biomedical Research Centre（NIHR穆尔菲尔德生物医学研究中心）；Technical University Munich（慕尼黑工业大学）；University of Southampton（南安普顿大学）
Biomediq A/S（Biomediq公司）；University of Copenhagen（哥本哈根大学）；University College London（伦敦大学学院）
Disease progression modeling (DPM) using longitudinal data is a challenging task in machine learning for healthcare that can provide clinicians with better tools for diagnosis and monitoring of disease. Existing DPM algorithms neglect temporal dependencies among measurements and make parametric assumptions about biomarker trajectories. In addition, they do not model multiple biomarkers jointly and need to align subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which…
A Temporal Machine Learning-Based Time-to-Event Model for Predicting ALS Progression and Healthcare Utilization
Harrison College of Pharmacy, Auburn University（奥本大学哈里森药学院）；Fisk University（菲斯克大学）；Lewis Katz School of Medicine, Temple University（天普大学刘易斯·卡茨医学院）；Emory University（埃默里大学）；Barnett College of Public Health, Temple University（天普大学巴尼特公共卫生学院）
Multi-level Phenotypic Models of Cardiovascular Disease and Obstructive Sleep Apnea Comorbidities: A Longitudinal Wisconsin Sleep Cohort Study
North Carolina A&T State University（北卡罗来纳农工州立大学）；University of South Florida（南佛罗里达大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Simultaneous column-based deep learning progression analysis of atrophy associated with AMD in longitudinal OCT studies
School of Computer Science and Engineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学计算机科学与工程学院）；The Alexander Grass Center for Bioengineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学亚历山大·格拉斯生物工程中心）；Hadassah Medical Center（哈达萨医疗中心）
Temporally Detailed Hypergraph Neural ODEs for Disease Progression Modeling
Department of Computer and Information Science and Engineering, University of Florida（佛罗里达大学计算机与信息科学与工程系）；Regenstrief Institute（Regenstrief研究所）；Department of Biostatistics and Health Data Science, Indiana University（印第安纳大学生物统计学与健康数据科学系）；Department of Pharmacy Practice, Purdue University（普渡大学药学实践系）
The longitudinal modeling of neuroanatomical changes related to Alzheimer's disease (AD) is crucial for studying the progression of the disease. To this end, we introduce TransforMesh, a spatio-temporal network based on transformers that models longitudinal shape changes on 3D anatomical meshes. While transformer and mesh networks have recently shown impressive performances in natural language processing and computer vision, their application to medical image analysis has been very limited. To the best of our knowledge, this is the first work that combines transformer and mesh networks. Our re…
Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum
University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；University of Pittsburgh（匹兹堡大学）；Eli and Lilly Company（礼来公司）；Washington University in St. Louis（圣路易斯华盛顿大学）
Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In this regard, there have been efforts to identify the risk of developing AD in its earliest time. While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression modeling (DPM). Under the same problem settings, in this work, we propose a novel computational framework that ca…
University of Copenhagen（哥本哈根大学）；Rigshospitalet（哥本哈根大学国立医院）
Medical imaging plays a vital role in modern diagnostics and treatment. The temporal nature of disease or treatment progression often results in longitudinal data. Due to the cost and potential harm, acquiring large medical datasets necessary for deep learning can be difficult. Medical image synthesis could help mitigate this problem. However, until now, the availability of GANs capable of synthesizing longitudinal volumetric data has been limited. To address this, we use the recent advances in latent space-based image editing to propose a novel joint learning scheme to explicitly embed tempor…
IBM Research（IBM研究院）；University of Colorado Denver（科罗拉多大学丹佛分校）；Lund University（隆德大学）
Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging fo…
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；The University of Georgia（佐治亚大学）
For decades, a variety of predictive approaches have been proposed and evaluated in terms of their prediction capability for Alzheimer's Disease (AD) and its precursor - mild cognitive impairment (MCI). Most of them focused on prediction or identification of statistical differences among different clinical groups or phases (e.g., longitudinal studies). The continuous nature of AD development and transition states between successive AD related stages have been overlooked, especially in binary or multi-class classification. Though a few progression models of AD have been studied recently, they w…
Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence
