MedCo Live Demo Tutorial
July 2021
Last updated
July 2021
Last updated
This tutorial is related to Version 3.0.0 of MedCo and to a synthetic dataset that simulates observations for 1500 patients in three hospitals. The synthetic data are derived from the ontology of the Swiss Personalized Oncology (SPO) program that is supported by the Swiss Personalized Health (SPHN) initiative.
Medco is a privacy-preserving federated-analytics platform for performing secure distributed analytics on data from several clinical sites. Medco is a project supported by the Swiss Personalized Health Network (SPHN) [1] and Data Protection for Personalized Health (DPPH) [2] initiatives and developed at Ecole Polytechnique Fédérale de Lausanne (EPFL) and Centre Hospitalier Universitaire Vaudois (CHUV). This project is also made possible by the joint efforts of the Hopitaux Universitaires de Genève (HUG) and Inselspital. Some terminologies available in this demo are in French, as the demo deployment is built from metadata available at CHUV. Other versions can be installed depending on the clinical site or researcher language. MedCo offers cross-compatibility between languages. It does not explain the technical details of the underlying technology or the deployment process. Please refer to the Medco website [3] for publications and technical details.
To demonstrate its wide range of possibilities, we present this tutorial that describes a few relevant uses-cases of Medco [4]. We will illustrate the Explore and Analysis functionalities. All the data in this demo are synthetic and do not belong to any company or institution.
Make a simple age-constrained cohort selection
Try a realistic oncology query and use the Medco-Analysis feature for survival curves
The Medco Live Demo client is available at the following address: medco-demo.epfl.ch/glowing-bear. The initial screen asks for credentials. For this demo, you can use these :
Username: test / Password: test
On the left side of the user interface is an ontology browser. The ontology browser enables you to explore the variables that might be contained in the database and to identify those that you would like to use for your query. Variables in the ontology browser are organized in a tree-like fashion and can be explored as a file system made of folders and files. Most of the time, variables and hierarchies are taken from standard medical terminologies and classifications. The purpose is to drag-and-drop criteria for cohort selection into the right-side panel called inclusion criteria.
1. In the MedCo Explore query parameters, select the option "Selected groups occur in the same instance". It allows to manually specify dependencies between criteria. This option will be explained later. 2. On the left sidebar, expand the ontology SPHN-SPO ontology, then the Birth Datetime group, and then expand it to reveal Birth Year-value. Drag-and-drop this Birth Year-value element to the right panel.
3. Below, an input field will appear: select greater than. 4. Next to this field, type 1980.
When you drag-and-drop an item, you can drop it in 3 different zones :
The Replace zone, to change the element
The Or zone, to create a Or condition with the new element
The And zone, to create a And condition with the new element
5. So for the next step, on the left sidebar, expand the Drug group, then the Drug_code, and finally ATC. Drag-and-drop the Nervous system drugs (N) element to the right panel in the And zone.
6. On the left sidebar, expand the SPOConcepts group, then the Somatic Variant Found group. Drag-and-drop the Gene_name element to the right panel in the And zone. 7. An input field will appear: select exactly matches. 8. Next to this field, type HRAS. 9. On the left sidebar, also in the Somatic Variant Food, drag-and-drop the Hgvs_c element to the right panel in the And zone. 10. An input field will appear: select contains. 11. Next to this field, type 6994.
The “Selected groups occur in the same instance” option we selected before enabled a checkbox next to each criterion. All the checked boxes require to refer to the same observation. On the contrary, unticked boxes refer to independent observations.
In this case, we require that the mutation on the gene HRAS and the mutation at the position 6994 are the same object. 12. So we need to uncheck the Same instance boxes for Birth Year-value and the Nervous system drugs (N) as the screenshot below. Indeed, these are distinct and independent objects from the Somatic Variant Found observation.
When all the Inclusion criteria have been added, the right panel should look like this :
Now that we have selected the Inclusion criteria, we can add the Exclusion criteria :
1. On the left sidebar, expand the Consent group, then Status. Drag-and-drop the Refused element to the right panel in the Exclusion area. Uncheck the option Same instance. 2. On the left sidebar, also in the Consent group, extend the Type group. Drag-and-drop the Waiver element to the right panel in the Exclusion’s Or zone.
3. Click Run. After a few seconds of loading, the number of subjects will be displayed at the top.
In this part, to have some subjects, we first need to build and run a Query, then we need to run some analyses on it. Finally, we can see the results of the analyses and change some visualization parameters.
1. Browse the ontology panel again to expand SPHN-SPO ontology, then expand SPOConcepts. Drag-and-drop the Oncology Drug Treatment element to the right panel in the Inclusion area.
2. Click Run at the top. After a few seconds of loading, the number of subjects will be displayed. 3. Name the Cohort as had_treatment and click on Save. The Cohort will appear on the left panel, below Saved Cohorts.
4. The Cohort is now saved, click on it to select it.
5. Click on the Analysis tab at the top of the page, then on Survival.
6. Open the Settings panel and set a Time Limit of 20 years.
7. Drag-and-drop the Oncology Drug Treatment element in the right panel under Start Event.
8. Browse the ontology panel again to expand Death Status, then expend Status. Drag-and-drop the Death element to the right panel under End Event.
9. Open the Subgroups panel and set the first subgroup name as surgery.
10. Drag-and-drop the Oncology Surgery element in the right panel in the Inclusion criteria area. Click on Save to save the subgroup.
11. Set the second subgroup name to no_surgery. 12. Drag-and-drop the Oncology Surgery element in the right panel in the Exclusion criteria area. Click on Save to save the subgroup.
13. Click on Run.
14. After a few seconds of loading, the result will be displayed. By opening the Input parameters panel, you can find more details about the query. 15. Click on the cog icon to open the panel to edit the Confidence interval of the graphical representation. In this panel, you can change diverse parameters to alter the graphical representation. When you have finished, close this panel.
16. On the right side, you can also choose the tabular scores to show.
17. Finally, you can download the results in PDF form by clicking on the download icon.
Only a few exploration and analysis features available in Medco have been presented in this document; more are available, and all can be combined with no limitations. No adaptations were made to the data, except for the tabular vs. graph representation. In particular, no links were lost or tampered with. Every edge in the semantic graphs (e.g., every relation between a patient and their diagnosis or treatment) is preserved. The Medco database uses visit (encounter) identifiers, patient pseudo-identifiers, and instance (observation) identifiers; they are not shown to the user. Therefore, using Medco does not inherently add any usability penalty, compared to the original clinical data.
[1] “Swiss personalized health network,” https://sphn.ch/, accessed: 2021-02-26.
[2] “Data protection in personal health,” https://dpph.ch/, accessed: 2021-02-26.
[3] https://medco.epfl.ch/, accessed: 2021-02-26.
[4] J. L. Raisaro, J. R. Troncoso-Pastoriza, M. Misbach, J. S. Sousa, S. Pradervand, E. Missiaglia, O. Michielin, B. Ford, and J.-P. Hubaux, “Medco: Enabling secure and privacy-preserving exploration of distributed clinical and genomic data,” IEEE/ACM transactions on computational biology and bioinformatics, vol. 16, no. 4, pp. 1328–1341, 2018.
