The Ontario Data Safe Haven: Bringing High Performance Computing to Population-wide Data Assets
Introduction Canadian provincial health systems have a data advantage – longitudinal population-wide data for publicly funded health services, in many cases going back 20 years or more. With the addition of high performance computing (HPC), these data can serve as the foundation for leading-edge res...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Swansea University
2018-08-01
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| Series: | International Journal of Population Data Science |
| Online Access: | https://ijpds.org/article/view/753 |
| _version_ | 1797430477518274560 |
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| author | J. Charles Victor P. Alison Paprica Michael Brudno Carl Virtanen Walter Wodchis Anna Goldenberg Michael Schull |
| author_facet | J. Charles Victor P. Alison Paprica Michael Brudno Carl Virtanen Walter Wodchis Anna Goldenberg Michael Schull |
| author_sort | J. Charles Victor |
| collection | DOAJ |
| description | Introduction
Canadian provincial health systems have a data advantage – longitudinal population-wide data for publicly funded health services, in many cases going back 20 years or more. With the addition of high performance computing (HPC), these data can serve as the foundation for leading-edge research using machine learning and artificial intelligence.
Objectives and Approach
The Institute for Clinical Evaluative Sciences (ICES) and HPC4Health are creating the Ontario Data Safe Haven (ODSH) – a secure HPC cloud located within the HPC4Health physical environment at the Hospital for Sick Children in Toronto. The ODSH will allow research teams to post, access and analyze individual datasets over which they have authority, and enable linkage to Ontario administrative and other data. To start, the ODSH is focused on creating a private cloud meeting ICES’ legislated privacy and security requirements to support HPC-intensive analyses of ICES data. The first ODSH projects are partnerships between ICES scientists and machine learning.
Results
As of March 2018, the technological build of the ODSH was tested and completed and the privacy and security policy framework and documentation were completed. We will present the structure of the ODSH, including the architectural choices made when designing the environment, and planned functionality in the future. We will describe the experience to-date for the very first analysis done using the ODSH: the automatic mining of clinical terminology in primary care electronic medical records using deep neural networks. We will also present the plans for a high-cost user Risk Dashboard program of research, co-designed by ICES scientists and health faculty from the Vector Institute for artificial intelligence, that will make use of the ODSH beginning May 2018.
Conclusion/Implications
Through a partnership of ICES, HPC4Health and the Vector Institute, a secure private cloud ODSH has been created as is starting to be used in leading edge machine learning research studies that make use of Ontario’s population-wide data assets. |
| first_indexed | 2024-03-09T09:28:09Z |
| format | Article |
| id | doaj.art-89a7a4bdc6094b4883ba1ff4955c5df1 |
| institution | Directory Open Access Journal |
| issn | 2399-4908 |
| language | English |
| last_indexed | 2024-03-09T09:28:09Z |
| publishDate | 2018-08-01 |
| publisher | Swansea University |
| record_format | Article |
| series | International Journal of Population Data Science |
| spelling | doaj.art-89a7a4bdc6094b4883ba1ff4955c5df12023-12-02T05:05:47ZengSwansea UniversityInternational Journal of Population Data Science2399-49082018-08-013410.23889/ijpds.v3i4.753The Ontario Data Safe Haven: Bringing High Performance Computing to Population-wide Data AssetsJ. Charles Victor0P. Alison Paprica1Michael Brudno2Carl Virtanen3Walter Wodchis4Anna Goldenberg5Michael Schull6Institute for Clinical Evaluative SciencesVector InstituteUniversity of TorontoHPC4HealthUniversity of TorontoThe Hospital for Sick ChildrenInstitute of Clinical Evaluative SciencesIntroduction Canadian provincial health systems have a data advantage – longitudinal population-wide data for publicly funded health services, in many cases going back 20 years or more. With the addition of high performance computing (HPC), these data can serve as the foundation for leading-edge research using machine learning and artificial intelligence. Objectives and Approach The Institute for Clinical Evaluative Sciences (ICES) and HPC4Health are creating the Ontario Data Safe Haven (ODSH) – a secure HPC cloud located within the HPC4Health physical environment at the Hospital for Sick Children in Toronto. The ODSH will allow research teams to post, access and analyze individual datasets over which they have authority, and enable linkage to Ontario administrative and other data. To start, the ODSH is focused on creating a private cloud meeting ICES’ legislated privacy and security requirements to support HPC-intensive analyses of ICES data. The first ODSH projects are partnerships between ICES scientists and machine learning. Results As of March 2018, the technological build of the ODSH was tested and completed and the privacy and security policy framework and documentation were completed. We will present the structure of the ODSH, including the architectural choices made when designing the environment, and planned functionality in the future. We will describe the experience to-date for the very first analysis done using the ODSH: the automatic mining of clinical terminology in primary care electronic medical records using deep neural networks. We will also present the plans for a high-cost user Risk Dashboard program of research, co-designed by ICES scientists and health faculty from the Vector Institute for artificial intelligence, that will make use of the ODSH beginning May 2018. Conclusion/Implications Through a partnership of ICES, HPC4Health and the Vector Institute, a secure private cloud ODSH has been created as is starting to be used in leading edge machine learning research studies that make use of Ontario’s population-wide data assets.https://ijpds.org/article/view/753 |
| spellingShingle | J. Charles Victor P. Alison Paprica Michael Brudno Carl Virtanen Walter Wodchis Anna Goldenberg Michael Schull The Ontario Data Safe Haven: Bringing High Performance Computing to Population-wide Data Assets International Journal of Population Data Science |
| title | The Ontario Data Safe Haven: Bringing High Performance Computing to Population-wide Data Assets |
| title_full | The Ontario Data Safe Haven: Bringing High Performance Computing to Population-wide Data Assets |
| title_fullStr | The Ontario Data Safe Haven: Bringing High Performance Computing to Population-wide Data Assets |
| title_full_unstemmed | The Ontario Data Safe Haven: Bringing High Performance Computing to Population-wide Data Assets |
| title_short | The Ontario Data Safe Haven: Bringing High Performance Computing to Population-wide Data Assets |
| title_sort | ontario data safe haven bringing high performance computing to population wide data assets |
| url | https://ijpds.org/article/view/753 |
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