Machine learning techniques typically applied to dementia forecasting lack in their capabilities to jointly learn several tasks, handle time dependent heterogeneous data and missing values. In this paper, we propose a framework using the recently presented SSHIBA model for jointly learning different tasks on longitudinal data with missing values. The method uses Bayesian variational inference to impute missing values and combine information of several views. This way, we can combine different data-views from different time-points in a common latent space and learn the relations between each time-point while simultaneously modelling and predicting several output variables. We apply this model to predict together diagnosis, ventricle volume, and clinical scores in dementia. The results demonstrate that SSHIBA is capable of learning a good imputation of the missing values and outperforming the baselines while simultaneously predicting three different tasks.
University of Oxford（牛津大学）；Alan Turing Institute（艾伦·图灵研究所）
Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. Despite the many advantages of joint modeling, the standard forms suffer from limitations that arise from a fixed model specification and computational difficulties when applied to large datasets. We adopt a deep learning approach to address these limitations, enhancing existing methods with the flexibility and scalability of deep neural networks while retaining the benefits of joint modeling. Using data from the Alzheimer's Disease Neuroimaging Institute, …
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…
Alzheimer's disease (AD) is the most common neurodegenerative disease in older people. Despite considerable efforts to find a cure for AD, there is a 99.6% failure rate of clinical trials for AD drugs, likely because AD patients cannot easily be identified at early stages. This project investigated machine learning approaches to predict the clinical state of patients in future years to benefit AD research. Clinical data from 1737 patients was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and was processed using the "All-Pairs" technique, a novel methodology crea…
Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories
Department of Mathematical Sciences, University of Texas at Dallas（德克萨斯大学达拉斯分校数学科学系）；Department of Plant Science and Landscape Architecture, University of Connecticut（康奈尔大学植物科学与景观建筑系）；Naveen Jindal School of Management, University of Texas at Dallas（德克萨斯大学达拉斯分校纳文·金达管理学院）；Zhejiang Provincial People’s Hospital（浙江省人民医院）
Computational models that forecast the progression of Alzheimer's disease at the patient level are extremely useful tools for identifying high risk cohorts for early intervention and treatment planning. The state-of-the-art work in this area proposes models that forecast by using latent representations extracted from the longitudinal data across multiple modalities, including volumetric information extracted from medical scans and demographic info. These models incorporate the time horizon, which is the amount of time between the last recorded visit and the future visit, by directly concatenat…
University of Tennessee（田纳西大学）；The University of Manchester（曼彻斯特大学）；Oak Ridge National Laboratory（橡树岭国家实验室）
Hidden Markov models (HMMs) are commonly used for disease progression modeling when the true patient health state is not fully known. Since HMMs typically have multiple local optima, incorporating additional patient covariates can improve parameter estimation and predictive performance. To allow for this, we develop hidden Markov recurrent neural networks (HMRNNs), a special case of recurrent neural networks that combine neural networks' flexibility with HMMs' interpretability. The HMRNN can be reduced to a standard HMM, with an identical likelihood function and parameter interpretations, but …
Bakar Computational Health Sciences Institute, University of California, San Francisco（加州大学旧金山分校巴卡尔计算健康科学研究所）
Clinical data for ambulatory care, which accounts for 90% of the nations healthcare spending, is characterized by relatively small sample sizes of longitudinal data, unequal spacing between visits for each patient, with unequal numbers of data points collected across patients. While deep learning has become state-of-the-art for sequence modeling, it is unknown which methods of time aggregation may be best suited for these challenging temporal use cases. Additionally, deep models are often considered uninterpretable by physicians which may prevent the clinical adoption, even of well performing …
NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
TUM University Hospital and Technical University of Munich (TUM)（慕尼黑工业大学医院与慕尼黑工业大学）；Institute for Diagnostic and Interventional Radiology, TUM University Hospital（慕尼黑工业大学医院诊断与介入放射学研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Institute for Diagnostic and Interventional Neuroradiology, TUM University Hospital（慕尼黑工业大学医院诊断与介入神经放射学研究所）；University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心）
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）
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…
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）
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）
OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology
Computational Sciences Center of Excellence, Roche, Penzberg, Germany（计算科学卓越中心，罗氏，彭茨贝格，德国）；Computational Health Center, Helmholtz Munich, Munich, Germany（计算健康中心，亥姆霍兹慕尼黑，慕尼黑，德国）；Department of Biology, Ludwig Maximilian University of Munich, Munich, Germany（生物学系，路德维希-马克西米利安-慕尼黑大学，慕尼黑，德国）；Computational Sciences Center of Excellence, Roche, Basel, Switzerland（计算科学卓越中心，罗氏，巴塞尔，瑞士）；Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Australia（生物化学与药理学系，Bio21分子科学与生物技术研究所，墨尔本大学，帕克维尔，澳大利亚）；Technical University of Munich, Munich, Germany（慕尼黑工业大学，慕尼黑，德国）
In recent years, longitudinal neuroimaging study has become increasingly popular in neuroscience research to investigate disease-related changes in brain functions. In current neuroscience literature, one of the most commonly used tools to extract and characterize brain functional networks is independent component analysis (ICA). However, existing ICA methods are not suited for modelling repeatedly measured imaging data. In this paper, we propose a novel longitudinal independent component model (L-ICA) which provides a formal modeling framework for extending ICA to longitudinal studies. By inc…
It is important to characterize the temporal trajectories of disease-related biomarkers in order to monitor progression and identify potential points of intervention. This is especially important for neurodegenerative diseases, as therapeutic intervention is most likely to be effective in the preclinical disease stages prior to significant neuronal damage. Longitudinal neuroimaging allows for the measurement of structural, functional, and metabolic integrity of the brain over time at the level of voxels. However, commonly used longitudinal analysis approaches, such as linear mixed effects mode…
Massachusetts Institute of Technology（麻省理工学院）；Imperial College London（伦敦帝国学院）
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by learning a population-level model using multi-modal data from previously seen patients using the base Gaussian Process (GP) regression. Then, this model is adapted sequentially over time to a new patient using domain adaptive GPs to form the patient's pGP. We show that this new approach, together with an auto-regressive formulation, leads to significant improvements …
We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data. The disease progression is modeled as a trajectory on a group of diffeomorphisms in the context of large deformation diffeomorphic metric mapping (LDDMM). We first exhibit the limited predictive abilities of geodesic regression extrapolation on this group. Building on the recent concept of parallel curves in shape manifolds, we then introduce a second predictive protocol which personalizes previously learned traj…
Institute for Applied Computational Science, Harvard John A. Paulson School of Engineering and Applied Sciences（哈佛大学约翰·A·保尔森工程与应用科学学院应用计算科学研究所）
Alzheimer's Disease (AD) ravages the cognitive ability of more than 5 million Americans and creates an enormous strain on the health care system. This paper proposes a machine learning predictive model for AD development without medical imaging and with fewer clinical visits and tests, in hopes of earlier and cheaper diagnoses. That earlier diagnoses could be critical in the effectiveness of any drug or medical treatment to cure this disease. Our model is trained and validated using demographic, biomarker and cognitive test data from two prominent research studies: Alzheimer's Disease Neuroima…
Biomediq A/S（Biomediq公司）；Cerebriu A/S（Cerebriu公司）；University of Copenhagen（哥本哈根大学）；University College London（伦敦大学学院）；King’s College London（伦敦国王学院）
Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of 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 hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values…
We study regularized estimation in high-dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an efficient algorithm for computing such estimates, based on proximal gradient descent. We apply our proposed technique to a longitudinal data set on Alzheimer's disease from the Cardiovascular Health Study Cognition Study, and use this data set to motivate and demonstrate several…
Cornell（康奈尔大学）；Electrical and Computer Eng, Cornell（康奈尔大学电气与计算机工程学院）
In this work, we consider the problem of predicting the course of a progressive disease, such as cancer or Alzheimer's. Progressive diseases often start with mild symptoms that might precede a diagnosis, and each patient follows their own trajectory. Patient trajectories exhibit wild variability, which can be associated with many factors such as genotype, age, or sex. An additional layer of complexity is that, in real life, the amount and type of data available for each patient can differ significantly. For example, for one patient we might have no prior history, whereas for another patient we…
A novel extrapolation method is proposed for longitudinal forecasting. A hierarchical Gaussian process model is used to combine nonlinear population change and individual memory of the past to make prediction. The prediction error is minimized through the hierarchical design. The method is further extended to joint modeling of continuous measurements and survival events. The baseline hazard, covariate and joint effects are conveniently modeled in this hierarchical structure. The estimation and inference are implemented in fully Bayesian framework using the objective and shrinkage priors. In si…
