Hypergraph Based Data Model for Complex Health Data Exploration and Its Implementation in PREDIMED Clinical Data Warehouse

International audienceWithin the PREDIMED Clinical Data Warehouse (CDW) of Grenoble Alpes University Hospital (CHUGA), we have developed a hypergraph based operational data model, aiming at empowering physicians to explore, visualize and qualitatively analyze interactively the complex and massive information of the patients treated in CHUGA. This model constitutes a central target structure, expressed in a dual form, both graphical and formal, which gathers the concepts and their semantic relations into a hypergraph whose implementation can easily be manipulated by medical experts. The implementation is based on a property graph database linked to an interactive graphical interface allowing to navigate through the data and to interact in real time with a search engine, visualization and analysis tools. This model and its agile implementation allow for easy structural changes inherent to the evolution of techniques and practices in the health field. This flexibility provides adaptability to the evolution of interoperability standards

0431 How can we avoid re-identification risk in big-data analysis? proposition of a new strategy of geographical subdivisions using gis tools

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Medico-administrative data combined with agricultural practices data to retrospectively estimate pesticide use by agricultural workers

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COVID-19 Geographical Maps and Clinical Data Warehouse PREDIMED

International audiencePREDIMED, Clinical Data Warehouse of Grenoble Alps University Hospital, is currently participating in daily COVID-19 epidemic follow-up via spatial and chronological analysis of geographical maps. This monitoring is aimed for cluster detection and vulnerable population discovery. Our real-time geographical representations allow us to track the epidemic both inside and outside the hospital

Does prenatal exposure to multiple airborne and tap-water pollutants increase neonatal thyroid-stimulating hormone concentrations? Data from the Picardy region, France

International audienceSystematic screening for congenital hypothyroidism by heel-stick sampling has revealed unexpected heterogeneity in the geographic distribution of newborn thyroid-stimulating hormone concentrations in Picardy, France. We explored a possible relationship with environmental pollutants. Methods: Zip code geolocation data from mothers of newborns without congenital hypothyroidism born in were linked to ecological data for a set of airborne (particulate matter with a diameter of 2.5 μm or less [PM 2.5 ] or 10 μm or less [PM 10 ]) and tap-water (nitrate and perchlorate ions and atrazine) pollutants. Statistical associations between mean exposure levels during the third trimester of pregnancy and Thyroid-stimulating hormone (TSH) concentrations in 6249 newborns (51 % male) were investigated using linear regression models. Results: Median neonatal TSH concentration (interquartile range, IQR) was 1.7 (1-2.8) mIU/L. An increase of one IQR in prenatal exposure to perchlorate ions (3.6 μg/L), nitrate ions (19.2 mg/L), PM 2.5 (3.7 μg/m 3) and PM (3.4 μg/m 3), were associated with increases in TSH concentrations of 2.30 % (95 % CI: 0.95-3.66), 5.84 % (95 % CI: 2.81-8.87), 13.44 % (95 % CI: 9.65-17.28) and 6.26 % (95 % CI: 3.01-9.56), respectively. Conclusions: Prenatal exposure to perchlorate and nitrate ions in tap water and to airborne PM over the third trimester of pregnancy was significantly associated with increased neonatal TSH concentrations

Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016–2018

Purpose In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the individual patient level as accurately as possible is one of the first steps towards improving patient outcomes. Risk assessment can enable healthcare providers to target resources to patients in greatest need through adaptations in processes and procedures. Electronic health data facilitates the application of machine-learning methods for risk analysis. We aim, first to reveal correlations between HAE occurrence and patients’ characteristics and/or the procedures they undergo during their hospitalisation, and second, to build models that allow the early identification of patients at an elevated risk of HAE.Participants 143 865 adult patients hospitalised at Grenoble Alpes University Hospital (France) between 1 January 2016 and 31 December 2018.Findings to date In this set-up phase of the project, we describe the preconditions for big data analysis using machine-learning methods. We present an overview of the retrospective de-identified multisource data for a 2-year period extracted from the hospital’s Clinical Data Warehouse, along with social determinants of health data from the National Institute of Statistics and Economic Studies, to be used in machine learning (artificial intelligence) training and validation. No supplementary information or evaluation on the part of medical staff will be required by the information system for risk assessment.Future plans We are using this data set to develop predictive models for several general HAEs including secondary intensive care admission, prolonged hospital stay, 7-day and 30-day re-hospitalisation, nosocomial bacterial infection, hospital-acquired venous thromboembolism, and in-hospital mortality

Cohort Creation and Visualization Using Graph Model in the PREDIMED Health Data Warehouse

International audienceGrenoble Alpes University Hospital (CHUGA) is currently deploying a health data warehouse called PREDIMED [1], a platform designed to integrate and analyze for research, education and institutional management the data of patients treated at CHUGA. PREDIMED contains healthcare data, administrative data and, potentially, data from external databases. PREDIMED is hosted by the CHUGA Information Systems Department and benefits from its strict security rules. CHUGA’s institutional project PREDIMED aims to collaborate with similar projects in France and worldwide. In this paper, we present how the data model defined to implement PREDIMED at CHUGA is useful for medical experts to interactively build a cohort of patients and to visualize this cohort