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.
Georgia Institute of Technology（佐治亚理工学院）；IBM Research（IBM研究院）；Vanderbilt University（范德堡大学）；Sutter Health（萨特健康）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Researchers require timely access to real-world longitudinal electronic health records (EHR) to develop, test, validate, and implement machine learning solutions that improve the quality and efficiency of healthcare. In contrast, health systems value deeply patient privacy and data security. De-identified EHRs do not adequately address the needs of health systems, as de-identified data are susceptible to re-identification and its volume is also limited. Synthetic EHRs offer a potential solution. In this paper, we propose EHR Variational Autoencoder (EVA) for synthesizing sequences of discrete …
EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records
Technical University of Munich（慕尼黑技术大学）；TUM School of Computation, Information and Technology（慕尼黑技术大学计算、信息与技术学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
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…
Disease progression modeling (DPM) involves using mathematical frameworks to quantitatively measure the severity of how certain disease progresses. DPM is useful in many ways such as predicting health state, categorizing disease stages, and assessing patients disease trajectory etc. Recently, with wider availability of electronic health records (EHR) and the broad application of data-driven machine learning method, DPM has attracted much attention yet remains two major challenges: (i) Due to the existence of irregularity, heterogeneity and long-term dependency in EHRs, most existing DPM method…
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 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…
Zhejiang University（浙江大学）；University of Oxford（牛津大学）
The recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has become a major concern that limits data sharing across hospital settings and subsequently hinders the advances in AI. Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data. However, the current generative models are limited as they only generate single type of clinical data for a synthetic patient, i.e., either continuo…
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）
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…
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…
Modeling patient disease progression using Electronic Health Records (EHRs) is critical to assist clinical decision making. While most of prior work has mainly focused on developing effective disease progression models using EHRs collected from an individual medical system, relatively little work has investigated building robust yet generalizable diagnosis models across different systems. In this work, we propose a general domain adaptation (DA) framework that tackles two categories of discrepancies in EHRs collected from different medical systems: one is caused by heterogeneous patient popula…
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 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）
Deep Survival Analysis of Longitudinal EHR Data for Joint Prediction of Hospitalization and Death in COPD Patients
B2SLab, Institut de Recerca i Innovació en Salut (IRIS), Universitat Politècnica de Catalunya（B2SLab、创新与健康研究所（IRIS）、巴塞罗那理工大学）；Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物医学研究网络（CIBER-BBN））；Institut de Recerca Sant Joan de Déu（圣约翰德德乌研究所）；Evidenze Health；Servei de Vigilància Epidemiològica i Resposta a Emergències de Salut Pública a Barcelona Sud, Subdirecció Regional a Barcelona, Secretaria de Salut Pública, Departament de Salut, Generalitat de Catalunya（巴塞罗那南部公共卫生流行病监测与应急响应服务，巴塞罗那地区副秘书处，公共卫生秘书处，加泰罗尼亚自治区卫生部门）；Fundació Docència i Recerca Mútua Terrassa, Catalonia, Spain（特拉维撒互惠教育与研究基金会，加泰罗尼亚，西班牙）
CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record Embeddings
Department of Computer Science and Engineering, University of North Texas（计算机科学与工程系，北卡罗来纳大学达顿分校）；BioDiscovery Institute, University of North Texas（生物发现研究所，北卡罗来纳大学达顿分校）；Center for Computational Life Sciences, University of North Texas（计算生命科学中心，北卡罗来纳大学达顿分校）
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）
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）
University of Pittsburgh（匹兹堡大学）；Beijing Information Science & Technology University（北京信息科技大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）；Astrata Solutions（阿斯特拉塔解决方案公司）
Heart failure (HF) is a major cause of mortality. Accurately monitoring HF progress and adjust therapies are critical for improving patient outcomes. An experienced cardiologist can make accurate HF stage diagnoses based on combination of symptoms, signs, and lab results from the electronic health records (EHR) of a patient, without directly measuring heart function. We examined whether machine learning models, more specifically the XGBoost model, can accurately predict patient stage based on EHR, and we further applied the SHapley Additive exPlanations (SHAP) framework to identify informative…
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）
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）
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 f…
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）
University of Delaware（特拉华大学）；Nemours Alfred I. duPont Hospital for Children（内穆尔·阿尔弗雷德·I·杜邦儿童医院）；Thomas Jefferson University（托马斯·杰斐逊大学）
Childhood obesity is a major public health challenge. Early prediction and identification of the children at a high risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this paper, we present a deep learning model designed for predic…
The increase in availability of longitudinal electronic health record (EHR) data is leading to improved understanding of diseases and discovery of novel phenotypes. The majority of clustering algorithms focus only on patient trajectories, yet patients with similar trajectories may have different outcomes. Finding subgroups of patients with different trajectories and outcomes can guide future drug development and improve recruitment to clinical trials. We develop a recurrent neural network autoencoder to cluster EHR data using reconstruction, outcome, and clustering losses which can be weighted…
Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Centre for Medical Informatics, University of Edinburgh（爱丁堡大学医学信息学中心）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；National Engineering Research Center for Software Engineering, Peking University（北京大学国家软件工程研究中心）；Health Data Research UK（英国健康数据研究中心）
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）
ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Technical University of Denmark（丹麦技术大学）；KTH Stockholm（瑞典皇家理工学院）；CIFAR Azrieli Global Scholar（CIFAR Azrieli 全球学者计划）；GlaxoSmithKline（葛兰素史克）
The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant advances in our understanding of health and disease with direct and immediate impact on the development of new diagnostics and therapeutic treatment options. However, access to EHRs is often restricted due to their perceived sensitive nature and associated legal concerns, and the cohorts therein typically are those seen at a specific hospital or network of hospitals and therefore not representative of the wider population of patients. Here, we…
Does Machine Learning Outperform Traditional Fibrosis Scores in Predicting Liver Cirrhosis Risk? A Longitudinal EHR-Based Study
Center for Health Systems Innovation, Oklahoma State University（俄克拉荷马州立大学健康系统创新中心）；Department of Management Science and Information Systems, Oklahoma State University（俄克拉荷马州立大学管理科学与信息系统系）；Department of Health Services Administration, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校健康服务管理系）；Division of Gastroenterology and Hepatology, Department of Medicine, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校消化内科与肝病科）；College of Medicine, The University of Oklahoma Health Campus（俄克拉荷马大学健康校园医学院）；Department of Family and Community Medicine, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校家庭与社区医学系）
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）
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…
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）
Time-Aware Attention for Enhanced Electronic Health Records Modeling
Department of Statistics and Data Science, National University of Singapore（统计与数据科学系，新加坡国立大学）；School of Computing, National University of Singapore（计算学院，新加坡国立大学）；Department of Biostatistics, Harvard T.H. Chan School of Public Health, USA（生物统计学系，哈佛T.H. Chan公共卫生学院，美国）
