Synthetic electronic health records generated with variational graph autoencoders

Abstract Data-driven medical care delivery must always respect patient privacy—a requirement that is not easily met. This issue has impeded improvements to healthcare software and has delayed the long-predicted prevalence of artificial intelligence in healthcare. Until now, it has been very difficult to share data between healthcare organizations, resulting in poor statistical models due to unrepresentative patient cohorts. Synthetic data, i.e., artificial but realistic electronic health records, could overcome the drought that is troubling the healthcare sector. Deep neural network architectures, in particular, have shown an incredible ability to learn from complex data sets and generate large amounts of unseen data points with the same statistical properties as the training data. Here, we present a generative neural network model that can create synthetic health records with realistic timelines. These clinical trajectories are generated on a per-patient basis and are represented as linear-sequence graphs of clinical events over time. We use a variational graph autoencoder (VGAE) to generate synthetic samples from real-world electronic health records. Our approach generates health records not seen in the training data. We show that these artificial patient trajectories are realistic and preserve patient privacy and can therefore support the safe sharing of data across organizations

RAQ: Relationship-Aware Graph Querying in Large Networks

The phenomenal growth of graph data from a wide variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node similarities to find matches of a given query graph in a (large) target graph. However, almost all existing techniques have tacitly ignored the presence of relationships in graphs, which are usually encoded through interactions between node and edge labels. In this paper, we propose RAQ-Relationship-Aware Graph Querying-to mitigate this gap. Given a query graph, RAQ identifies the k best matching subgraphs of the target graph that encode similar relationships as in the query graph. To assess the utility of RAQ as a graph querying paradigm for knowledge discovery and exploration tasks, we perform a user survey on the Internet Movie Database (IMDb), where an overwhelming 86% of the 170 surveyed users preferred the relationship-aware match over traditional graph querying. The need to perform subgraph isomorphism renders RAQ NP-hard. The querying is made practical through beam stack search. Extensive experiments on multiple real-world graph datasets demonstrate RAQ to be effective, efficient, and scalable

How breast cancer treatments affect the quality of life of women with non-metastatic breast cancer one year after surgical treatment: A cross-sectional study in Greece

Background: The continuously increasing survivorship of female breast cancer makes the monitoring and improvement of patients' quality of life ever so important. While globally there is a growing body of research on health-related quality of life 1 year after surgical treatment for non-metastatic breast cancer, up-to-date information regarding Greek patients is scarce. Objective: To measure the level of QoL of non-metastatic BC survivors in Greece 1 year after surgery. Methods: A sample of 200 female breast cancer survivors aged 18 to 75, who followed up as outpatients in five public hospitals were included in this cross-sectional study. All recruited patients agreed to participate in the study (100% response rate). Quality of life data were collected through the EORTC QLQ-C30 as well as BR23 questionnaires. Results: Cronbach's alpha for all scales of the two questionnaires was from 0.551 to 0.936 indicating very good reliability. According to the Multiple Linear Regression, older patients showed a lower future perspective (p =.031), with those living in rural areas, which was associated with more financial difficulties (p =.001). Women with tertiary education and those who had been hospitalized in a university hospital recorded better on global health status (p =.003 and.000 respectively). Patients who underwent chemotherapy reported better scores in the emotional function sub-scale (p =.025). Women with reconstruction and at least one complication appeared to have significantly better scores in future perspective and social function (p =.005,.002 respectively). Conclusions: Breast cancer survivors were found to have an overall good quality of life, functioning/symptoms scores and were satisfied with the provided care. © 2020 The Author(s)

Can existing theories of health care reform explain the Greek case (1983—2001)?

Greece has enacted three major health care reforms since the National Health System (NHS) was established in 1983. These reforms were designed to improve the system's ability to realize its founding principles of equity and efficiency in the delivery and financing of health services. This article presents an early report of ongoing doctoral research that aims to examine the relative influence of medical professional organizations versus other interests on these reforms. The article outlines three theoretical frameworks for understanding the health care system and the role of the medical profession within it in order to establish which best explains the nature and extent of health care reform. These frameworks are: sociological theories of professions; historical institutionalism; and structural interest theory. </jats:p