Why health data research infrastructures are set up to fail Why health data research infrastructures are set up to fail
(17 January 2018)
Wearables platform RADAR-base: An mHealth platform for Generalised Data Collection
(5 April 2018)
From question to publication in 5 days – How EHDEN and OHDSI change medical evidence generation through open science
(28 March 2019)
Open Insights Seminar at Harvard DBMI: GO-FAIR on biomedical data with the Personal Health Train
(15 February 2018)
Origins and perspectives for FAIR Data with DTL Origins and perspectives for FAIR Data
(16 November 2018)
Reducing cancer deaths and complications by leveraging OMOP and OHDSI (EHDEN Webinar with Ismail Gogenur)
(4 September 2019)
Applying the OMOP data model & OHDSI software to national European health data registries: the IMI EMIF project
Talk at SCOPE Summit 2017 – Real World Data track
Description: A large open source initiative for standardisation and epidemiological analysis for real world data is OHDSI: Observational Health Data Sciences and Informatics. OHDSI leverages the OMOP common data model for observational data, and provides data analysis tools for a broad range of use cases. This talk will explain OMOP and OHDSI with case study IMI EMIF, in which health data from over 50 million patients from 13 national and regional European registries is brought together.
Tackling the Clinical Data Challenges When Analyzing a Million Genomes
Talk at BioIT World 2019
Description: Population genetics and genomics is an emerging topic for the application of machine learning methods in healthcare and biomedical sciences. Currently, several large genomics initiatives, such as Genomics England, UK Biobank, the All of Us Project, and Europe’s 1 Million Genomes Initiative are all in the process of making both clinical and genomics data available from large numbers of patients to benefit biomedical research. However, a key challenge in these initiatives is the standardization of the clinical and outcomes data in such a way that machine learning methods can be effectively trained to discover useful medical and scientific insights. In this talk, we will look at what data is available at scale, and review some of examples of the application of common data and evidence models such as OMOP, FHIR, GA4GH etc. in order to achieve this, based on projects which The Hyve has executed with some of these initiatives to harmonize their clinical, genomics, imaging and wearables data and make it FAIR.
Talk at Pharmaceutical IT & Data Congress 2019, Proventa International’s Bioinformatics East Coast Strategy meeting 2019
- Fairspace: a new cloud service to enable collaborative science
- Implementation of FAIR in practice: which of the 15 principles to start with and what’s the RoI?
- Common Data Models: OMOP/OHDSI, i2b2/tranSMART, CDISC, FHIR, etc: how do they relate, and which one to choose
Description: Glowing Bear is a cohort selection user interface for the TranSMART clinical data warehouse. In recent years, features for several use cases have been added: time series data, standard ontologies, family relations, sample-level lab data. Meanwhile, the structure of the platform has been transformed to be more modular and maintainable. We give an overview of the added features and the changes to the data model and architecture.
Description: Deployment of tranSMART and all its dependencies used to be a complex task, mainly because of many dependencies, different versions and configuration options. With the new structure of the platform, dockerization of all its components and a main compose scripts it is not only faster to deploy everything, but also easier to manage the configuration, ensure security and monitor the components.
Description: An overview of data loading tools to tranSMART 17.X for Jupyter Notebook and automated ETL pipelines
Large scale observational clinical research with OHDSI
Talk at i2b2 tranSMART Tübingen Symposium 2019
Description: Observational Health Data Sciences and Informatics (OHDSI) is a multi-stakeholder, interdisciplinary, international collaborative with a mission to improve health by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care. With 200 researchers from 25 countries and half a billion unique patients, OHDSI carries out federated studies at sufficient scale to answer questions about diagnosis and treatment. At the heart of the OHDSI platform is the OMOP Common Data Model, currently at v6, around which a toolset is built for carrying out reusable, repeatable and reproducible observational clinical research on a large scale.
Choosing a Common Data Model for FAIR Biomedical Data
PharmaTec 2019 Pre-event Newsletter