Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging. The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above. Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics. We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and glossary, 51 in total. Inclusion of a large number of recent publications and expansion of the discussion accordingl

DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

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 for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic

Health Information Technology and Accountable Care Organizations: A Systematic Review and Future Directions

Background: Since the inception of Accountable Care Organizations (ACOs), many have acknowledged the potential synergy between ACOs and health information technology (IT) in meeting quality and cost goals. Objective: We conducted a systematic review of the literature in order to describe what research has been conducted at the intersection of health IT and ACOs and identify directions for future research. Methods: We identified empirical studies discussing the use of health IT via PubMed search with subsequent snowball reference review. The type of health IT, how health IT was included in the study, use of theory, population, and findings were extracted from each study. Results: Our search resulted in 32 studies describing the intersection of health IT and ACOs, mainly in the form of electronic health records and health information exchange. Studies were divided into three streams by purpose; those that considered health IT as a factor for ACO participation, health IT use by current ACOs, and ACO performance as a function of health IT capabilities. Although most studies found a positive association between health IT and ACO participation, studies that address the performance of ACOs in terms of their health IT capabilities show more mixed results. Conclusions: In order to better understand this emerging relationship between health IT and ACO performance, we propose future research should consider more quasi-experimental studies, the use of theory, and merging health, quality, cost, and health IT use data across ACO member organizations

"Whose data is it anyway?" The implications of putting small area-level health and social data online

International audienceThe planetary exospheres are poorly known in their outer parts, since the neutral densities are low compared with the instruments detection capabilities. The exospheric models are thus often the main source of information at such high altitudes. We present a new way to take into account analytically the additional effect of the radiation pressure on planetary exospheres. In a series of papers, we present with an Hamiltonian approach the effect of the radiation pressure on dynamical trajectories, density profiles and escaping thermal flux. Our work is a generalization of the study by Bishop and Chamberlain (1989). In this second part of our work, we present here the density profiles of atomic Hydrogen in planetary exospheres subject to the radiation pressure. We first provide the altitude profiles of ballistic particles (the dominant exospheric population in most cases), which exhibit strong asymmetries that explain the known geotail phenomenon at Earth. The radiation pressure strongly enhances the densities compared with the pure gravity case (i.e. the Chamberlain profiles), in particular at noon and midnight. We finally show the existence of an exopause that appears naturally as the external limit for bounded particles, above which all particles are escaping