Difference between revisions of "File:Fig9 Kapoor JofAppCliMedPhys2023 24-10.jpg"
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==Summary== | |||
{{Information | |||
|Description='''Figure 9.''' Overall architecture of our radiation oncology learning health system (RO-LHS) infrastructure. Here we have the data captured at care delivery from the three data sources and the informatics layer to extract, transform, and load this data based on standard taxonomy and ontologies into the RO-LHS core data repository. This repository is the RDF graph database that stores the data with established definitions and relationships based on the standard terminology and ontology. The data listed in the RO-LHS is made available for subsequent applications such as quality measure analysis, cohort identification, continuous quality improvement, and building ML models that can be applied back to the care delivery to improve care, thus completing the loop for an effective learning health system. | |||
|Source={{cite journal |title=Infrastructure tools to support an effective radiation oncology learning health system |journal=Journal of Applied Clinical Medical Physics |author=Kapoor, Rishabh; Sleeman IV, William C.; Ghosh, Preetam; Palta, Jatinder |volume=24 |issue=10 |at=e14127 |year=2023 |doi=10.1002/acm2.14127}} | |||
|Author=Kapoor, Rishabh; Sleeman IV, William C.; Ghosh, Preetam; Palta, Jatinder | |||
|Date=2023 | |||
|Permission=[https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International] | |||
}} | |||
== Licensing == | == Licensing == | ||
{{cc-by-4.0}} | {{cc-by-4.0}} | ||
Latest revision as of 18:25, 10 May 2024
Summary
| Description |
Figure 9. Overall architecture of our radiation oncology learning health system (RO-LHS) infrastructure. Here we have the data captured at care delivery from the three data sources and the informatics layer to extract, transform, and load this data based on standard taxonomy and ontologies into the RO-LHS core data repository. This repository is the RDF graph database that stores the data with established definitions and relationships based on the standard terminology and ontology. The data listed in the RO-LHS is made available for subsequent applications such as quality measure analysis, cohort identification, continuous quality improvement, and building ML models that can be applied back to the care delivery to improve care, thus completing the loop for an effective learning health system. |
|---|---|
| Source |
Kapoor, Rishabh; Sleeman IV, William C.; Ghosh, Preetam; Palta, Jatinder (2023). "Infrastructure tools to support an effective radiation oncology learning health system". Journal of Applied Clinical Medical Physics 24 (10): e14127. doi:10.1002/acm2.14127. |
| Date |
2023 |
| Author |
Kapoor, Rishabh; Sleeman IV, William C.; Ghosh, Preetam; Palta, Jatinder |
| Permission (Reusing this file) |
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| Other versions |
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This work is licensed under the Creative Commons Attribution 4.0 License. |
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