Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data
Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands（埃因霍温麦斯特大学放射学与核医学系）；Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands（埃因霍温麦斯特大学流行病学系）；Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom（伦敦大学学院霍克斯研究所）
Sterre de Jonge, Elisabeth J. Vinke, Meike W. Vernooij, Daniel C. Alexander, Alexandra L. Young, Esther E. Bron
备注：
Accepted for publication, 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), April 2026, London, United Kingdom
Disease progression modeling provides a robust framework to identify long-term disease trajectories from short-term biomarker data. It is a valuable tool to gain a deeper understanding of diseases with a long disease trajectory, such as Alzheimer's disease. A key limitation of most disease progression models is that they are specific to a single data type (e.g., continuous data), thereby limiting their applicability to heterogeneous, real-world datasets. To address this limitation, we propose the Mixed Events model, a novel disease progression model that handles both discrete and continuous data types. This model is implemented within the Subtype and Stage Inference (SuStaIn) framework, resulting in Mixed-SuStaIn, enabling subtype and progression modeling. We demonstrate the effectiveness of Mixed-SuStaIn through simulation experiments and real-world data from the Alzheimer's Disease Neuroimaging Initiative, showing that it performs well on mixed datasets. The code is available at: https://github.com/ucl-pond/pySuStaIn.
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…
RWTH Aachen University（亚琛工业大学）；University of Edinburgh（爱丁堡大学）；QuantumBlack；McGill University（麦吉尔大学）
Identification of disease subtypes and corresponding biomarkers can substantially improve clinical diagnosis and treatment selection. Discovering these subtypes in noisy, high dimensional biomedical data is often impossible for humans and challenging for machines. We introduce a new approach to facilitate the discovery of disease subtypes: Instead of analyzing the original data, we train a diagnostic classifier (healthy vs. diseased) and extract instance-wise explanations for the classifier's decisions. The distribution of instances in the explanation space of our diagnostic classifier amplifi…
Breast cancer's complexity and variability pose significant challenges in understanding its progression and guiding effective treatment. This study aims to integrate protein sequence data with expression levels to improve the molecular characterization of breast cancer subtypes and predict clinical outcomes. Using ProtGPT2, a language model specifically designed for protein sequences, we generated embeddings that capture the functional and structural properties of proteins. These embeddings were integrated with protein expression levels to form enriched biological representations, which were a…
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
Bioinformatics Center, Kyoto University（京都大学生物信息学中心）；SANKEN, Osaka University（大阪大学SANKEN（大阪大学蛋白质研究所））；National Institute of Advanced Industrial Science and Technology (AIST)（日本国立先进工业科学技术研究所）；RIKEN Center for Computational Science（理化学研究所计算科学中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
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…
We model Alzheimer's disease (AD) progression by combining differential equations (DEs) and reinforcement learning (RL) with domain knowledge. DEs provide relationships between some, but not all, factors relevant to AD. We assume that the missing relationships must satisfy general criteria about the working of the brain, for e.g., maximizing cognition while minimizing the cost of supporting cognition. This allows us to extract the missing relationships by using RL to optimize an objective (reward) function that captures the above criteria. We use our model consisting of DEs (as a simulator) an…
University of Pennsylvania（宾夕法尼亚大学）；University of Southern California（南加州大学）；Lausanne University Hospital (CHUV) and University of Lausanne（洛桑大学医院（CHUV）和洛桑大学）；University of Texas San Antonio Health Science Center（德克萨斯大学圣安东尼奥健康科学中心）；The University of Melbourne（墨尔本大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；National Institute on Aging, National Institutes of Health（美国国立卫生研究院国家老龄化研究所）；Johns Hopkins University（约翰霍普金斯大学）；University of Wisconsin（威斯康星大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；National Institute on Aging（国家老龄化研究所）
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects dis…
Unscrambling disease progression at scale: fast inference of event permutations with optimal transport
Sussex AI Centre Department of Informatics University of Sussex（苏塞克斯大学人工智能中心信息学院）；Hawkes Institute Department of Computer Science University College London（霍克斯研究所计算机科学学院）
Macau University of Science and Technology（澳门科技大学）
Current machine learning has made great progress on computer vision and many other fields attributed to the large amount of high-quality training samples, while it does not work very well on genomic data analysis, since they are notoriously known as small data. In our work, we focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients that can guide treatment decisions for a specific individual through training on small data. In fact, doctors and clinicians always address this problem by studying several interrelated clinical variables simultaneously. We at…
Progressive diseases worsen over time and are characterised by monotonic change in features that track disease progression. Here we connect ideas from two formerly separate methodologies -- event-based and hidden Markov modelling -- to derive a new generative model of disease progression. Our model can uniquely infer the most likely group-level sequence and timing of events (natural history) from limited datasets. Moreover, it can infer and predict individual-level trajectories (prognosis) even when data are missing, giving it high clinical utility. Here we derive the model and provide an infe…
Identifying factors associated with fast visual field progression in patients with ocular hypertension based on unsupervised machine learning
University of Tennessee Health Science Center（田纳西大学健康科学中心）；National Institutes of Health (NIH)（美国国立卫生研究院）；Massachusetts Eye and Ear（马萨诸塞州眼耳医院）；University of Iowa Hospitals and Clinics（爱荷华大学医院和诊所）
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…
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）
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 …
Unifying Interpretability and Explainability for Alzheimer's Disease Progression Prediction
Kansas State University（堪萨斯州立大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Kansas Medical Center（堪萨斯大学医学中心）；Alzheimer’s Disease Research Center（阿尔茨海默病研究中心）
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））
Erasmus MC, University Medical Center Rotterdam（鹿特丹伊拉斯姆斯医学中心（鹿特丹大学医学中心））；Delft University of Technology（代尔夫特理工大学）
Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown. Event-based modeling (EBM) is a data-driven technique to estimate the sequence in which biomarkers for a disease become abnormal based on cross-sectional data. It can help in understanding the dynamics of disease progression and facilitate early diagnosis and prognosis. In this work we propose a novel discriminative approach to EBM, which is shown to be more accurate than existing state-of-the-art EBM methods. The method first estimates for e…
Seoul National University（首尔国立大学）；Bioinformatics Institute, Seoul National University（首尔国立大学生物信息学研究所）
Network biology has been successfully used to help reveal complex mechanisms of disease, especially cancer. On the other hand, network biology requires in-depth knowledge to construct disease-specific networks, but our current knowledge is very limited even with the recent advances in human cancer biology. Deep learning has shown a great potential to address the difficult situation like this. However, deep learning technologies conventionally use grid-like structured data, thus application of deep learning technologies to the classification of human disease subtypes is yet to be explored. Rece…
In this thesis the aim is to work on optimizing the modern machine learning models for personalized forecasting of Alzheimer Disease (AD) Progression from clinical trial data. The data comes from the TADPOLE challenge, which is one of the largest publicly available datasets for AD research (ADNI dataset). The goal of the project is to develop machine learning models that can be used to perform personalized forecasts of the participants cognitive changes (e.g., ADAS-Cog13 scores) over the time period of 6,12, 18 and 24 months in the future and the change in Clinical Status (CS) i.e., whether a …
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）
University of Pennsylvania（宾夕法尼亚大学）；Columbia University（哥伦比亚大学）；Washington University in St. Louis（圣路易斯华盛顿大学）
There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimers Disease (AD). Elucidating distinct subtypes of diseases allows a better understanding of neuropathogenesis and enables the possibility of developing targeted treatment programs. Recent semi-supervised clustering techniques have provided a data-driven way to understand disease heterogeneity. However, existing methods do not take into account that subtypes of the disease might present themselves at dif…
Intense debate in the Neurology community before 2010 culminated in hypothetical models of Alzheimer's disease progression: a pathophysiological cascade of biomarkers, each dynamic for only a segment of the full disease timeline. Inspired by this, data-driven disease progression modelling emerged from the computer science community with the aim to reconstruct neurodegenerative disease timelines using data from large cohorts of patients, healthy controls, and prodromal/at-risk individuals. This chapter describes selected highlights from the field, with a focus on utility for understanding and f…
Patient subtyping based on temporal observations can lead to significantly nuanced subtyping that acknowledges the dynamic characteristics of diseases. Existing methods for subtyping trajectories treat the evolution of clinical observations as a homogeneous process or employ data available at regular intervals. In reality, diseases may have transient underlying states and a state-dependent observation pattern. In our paper, we present an approach to subtype irregular patient data while acknowledging the underlying progression of disease states. Our approach consists of two components: a probab…
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…
Alzheimer's Disease (AD), as the most devastating neurodegenerative disease worldwide, has reached nearly 10 million new cases annually. Current technology provides unprecedented opportunities to study the progression and etiology of this disease with the advanced in imaging techniques. With the recent emergence of a society driven by big data and machine learning (ML), researchers have exerted considerable effort to summarize recent advances in ML-based AD diagnosis. Here, we outline some of the most prevalent and recent ML models for assessing the progression of AD and provide insights on th…
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.
In this work we introduce Disease Progression Modeling workbench 360 (DPM360) opensource clinical informatics framework for collaborative research and delivery of healthcare AI. DPM360, when fully developed, will manage the entire modeling life cycle, from data analysis (e.g., cohort identification) to machine learning algorithm development and prototyping. DPM360 augments the advantages of data model standardization and tooling (OMOP-CDM, Athena, ATLAS) provided by the widely-adopted OHDSI initiative with a powerful machine learning training framework, and a mechanism for rapid prototyping th…
Alzheimer's Disease (AD) is a neurodegenerative disease that affects subjects in a broad range of severity and is assessed in clinical trials with multiple cognitive and functional instruments. As clinical trials in AD increasingly focus on earlier stages of the disease, especially Mild Cognitive Impairment (MCI), the ability to model subject outcomes across the disease spectrum is extremely important. We use unsupervised machine learning models called Conditional Restricted Boltzmann Machines (CRBMs) to create Digital Twins of AD subjects. Digital Twins are simulated clinical records that sha…
Ability to quantify and predict progression of a disease is fundamental for selecting an appropriate treatment. Many clinical metrics cannot be acquired frequently either because of their cost (e.g. MRI, gait analysis) or because they are inconvenient or harmful to a patient (e.g. biopsy, x-ray). In such scenarios, in order to estimate individual trajectories of disease progression, it is advantageous to leverage similarities between patients, i.e. the covariance of trajectories, and find a latent representation of progression. Most of existing methods for estimating trajectories do not accoun…
Most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative that uses unsupervised deep learning to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1908 patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteri…
TransFair: Transferring Fairness from Ocular Disease Classification to Progression Prediction
School of Computing and Informatics, University of Louisiana at Lafayette（路易斯安那大学拉斐特分校计算与信息学院）；Informatics Research Institute, University of Louisiana at Lafayette（路易斯安那大学拉斐特分校信息学研究所）；College of Engineering and Computer Science, Florida Atlantic University（佛罗里达大西洋大学工程与计算机科学学院）；Harvard Medical School（哈佛医学院）
Erasmus MC, University Medical Center Rotterdam（鹿特丹大学医学中心伊拉斯谟医学中心）；Delft University of Technology（代尔夫特理工大学）
Alzheimer's disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM). Since DEBM is a data-driven model, stratification into smaller disease subgroups would lead to more inaccurate models as compared to fitting the model on the entire dataset. Hence our secondary aim is to propose and evaluate novel approaches in which we split the different st…
In this policy paper, we implement the epidemiological SIR to estimate the basic reproduction number $\mathcal{R}_0$ at national and state level. We also developed the statistical machine learning model to predict the cases ahead of time. Our analysis indicates that the situation of Punjab ($\mathcal{R}_0\approx 16$) is not good. It requires immediate aggressive attention. We see the $\mathcal{R}_0$ for Madhya Pradesh (3.37) , Maharastra (3.25) and Tamil Nadu (3.09) are more than 3. The $\mathcal{R}_0$ of Andhra Pradesh (2.96), Delhi (2.82) and West Bengal (2.77) is more than the India's $\mat…
PIE: Simulating Disease Progression via Progressive Image Editing
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California San Diego（加利福尼亚大学圣迭戈分校）；University of California, Berkeley（加利福尼亚大学伯克利分校）；University of Virginia（弗吉尼亚大学）；PediaMed AI；Northwestern University（西北大学）；Carnegie Mellon University（卡内基梅隆大学）
