Deep learning models have demonstrated high-quality performance in areas such as image classification and speech processing. However, creating a deep learning model using electronic health record (EHR) data, requires addressing particular privacy challenges that are unique to researchers in this domain. This matter focuses attention on generating realistic synthetic data while ensuring privacy. In this paper, we propose a novel framework called correlation-capturing Generative Adversarial Network (CorGAN), to generate synthetic healthcare records. In CorGAN we utilize Convolutional Neural Networks to capture the correlations between adjacent medical features in the data representation space by combining Convolutional Generative Adversarial Networks and Convolutional Autoencoders. To demonstrate the model fidelity, we show that CorGAN generates synthetic data with performance similar to that of real data in various Machine Learning settings such as classification and prediction. We also give a privacy assessment and report on statistical analysis regarding realistic characteristics of the synthetic data. The software of this work is open-source and is available at: https://github.com/astorfi/cor-gan.
Peking University（北京大学）；Lancaster University（兰卡斯特大学）；Peking University Third Hospital（北京大学第三医院）
Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based solutions for EMR analysis concentrate on learning the clinical visit embedding and exploring the relations between visits. Although those works have shown superior performances in healthcare prediction, they fail to explore the personal characteristics during the clinical visits thoroughly. Moreover, existing works usually assume that the more recent record weights more in the prediction, but this assumption is not su…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Due to potential applications in chronic disease management and personalized healthcare, the EHRs data analysis has attracted much attention of both researchers and practitioners. There are three main challenges in modeling longitudinal and heterogeneous EHRs data: heterogeneity, irregular temporality and interpretability. A series of deep learning methods have made remarkable progress in resolving these challenges. Nevertheless, most of existing attention models rely on capturing the 1-order temporal dependencies or 2-order multimodal relationships among feature elements. In this paper, we pr…
Multivariate time series (MTS) have become increasingly common in healthcare domains where human vital signs and laboratory results are collected for predictive diagnosis. Recently, there have been increasing efforts to visualize healthcare MTS data based on star charts or parallel coordinates. However, such techniques might not be ideal for visualizing a large MTS dataset, since it is difficult to obtain insights or interpretations due to the inherent high dimensionality of MTS. In this paper, we propose 'm-TSNE': a simple and novel framework to visualize high-dimensional MTS data by projecti…
Characterizing Physician Referral Networks with Ricci Curvature
Technical University of Munich（慕尼黑技术大学）；School of Computation, Information and Technology（计算、信息与技术学院）；Institute of AI for Health, Helmholtz Munich（健康人工智能研究所，海德堡-慕尼黑）；Department of Informatics, University of Fribourg（信息学院，弗里堡大学）；Carlson School of Management（卡尔森管理学院）；University of Minnesota（明尼苏达大学）
DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare Data
IUCT-Oncopole Institut Claudius Regaud（克劳迪乌斯·雷戈德研究所肿瘤学中心）；IRT Saint Exupéry（圣埃克苏佩里技术研究院）；INSERM UMR 1037, Cancer Research Center of Toulouse (CRCT)（法国国家健康与医学研究院UMR 1037，图卢兹癌症研究中心）
Analysing human motions is a core topic of interest for many disciplines, from Human-Computer Interaction, to entertainment, Virtual Reality and healthcare. Deep learning has achieved impressive results in capturing human pose in real-time. On the other hand, due to high inter-subject variability, human motion analysis models often suffer from not being able to generalise to data from unseen subjects due to very limited specialised datasets available in fields such as healthcare. However, acquiring human motion datasets is highly time-consuming, challenging, and expensive. Hence, human motion …
Harvard University（哈佛大学）；University of Ljubljana（卢布尔雅那大学）；MIT Lincoln Laboratory（麻省理工学院林肯实验室）
In many domains, including healthcare, biology, and climate science, time series are irregularly sampled with varying time intervals between successive readouts and different subsets of variables (sensors) observed at different time points. Here, we introduce RAINDROP, a graph neural network that embeds irregularly sampled and multivariate time series while also learning the dynamics of sensors purely from observational data. RAINDROP represents every sample as a separate sensor graph and models time-varying dependencies between sensors with a novel message passing operator. It estimates the l…
Centre for Pattern Recognition and Data Analytics（模式识别与数据分析中心）；Deakin University（迪肯大学）
Understanding the latent processes from Electronic Medical Records could be a game changer in modern healthcare. However, the processes are complex due to the interaction between at least three dynamic components: the illness, the care and the recording practice. Existing methods are inadequate in capturing the dynamic structure of care. We propose an end-to-end model that reads medical record and predicts future risk. The model adopts the algebraic view in that discrete medical objects are embedded into continuous vectors lying in the same space. The bag of disease and comorbidities recorded …
CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing
Birmingham City University（伯明翰城市大学）；Helen and John C. Hartmann Department of Electrical and Computer Engineering（海伦和约翰·C·哈特曼电气与计算机工程系）；New Jersey Institute of Technology（新泽西理工学院）
