1   Local Data Storage

Typically in vanilla vantage6, the data is fetched from the data source for each computation call. This made computations slow as the OMOP query was typically time consuming. To speed up the computations, I decided to fetch the data once for each cohort and store it local in the vantage6 node.

┌──────────┐   ┌────────────┐   ┌────────────┐
│ OMOP     │   │ Query      │   │ Local DB   │
│ Database ├──►│ Algorithm  ├──►│ Parquet    │
└──────────┘   └────────────┘   └────────────┘

The Query Algorithm is a vantage6 algorithm that is responsible for fetching the data from the OMOP database. It creates the ATLAS cohort and reads the patient features. The data is stored by this algorithm in a Parquet file. This Parquet file is then used by the other algorithms to perform the analytics.

┌────────────┐   ┌────────────┐   ┌───────────┐
│ Local DB   │   │ vantage6   │   │ Algorithm │
│ Parquet    ├──►│ Algorithm  ├──►│ Output    │
└────────────┘   └────────────┘   └───────────┘

There are some challenges with this approach:

• When a node is offline when a new cohort is created it will not be able to fetch the data. In this case, the node will create the cohort data it comes online. The user can work with the other nodes in the meantime.
• When the data source is updated, the Parquet files need to be updated as well. This is currently a manual process as the user needs to trigger the Query Algorithm to fetch the data again.
• The Parquet files need to have the same variables and the same value types for these variables. This should be guaranteed by the Query Algorithm. Especially when the cohorts are not created at the same time (e.g. when a node was offline when it was created).

When a node is offline when a new cohort is created it will not be able to fetch the data. In this case, the node will create the cohort data it comes online. The user can work with the other nodes in the meantime.

2   Researcher User Interface

The official vantage6 User Interface (UI) is developed as a general-purpose vantage6 UI.

The official vantage6 user interface from vantage6 (from https://vantage6.ai).

If a new feature is to be added in this interface, it needs to be compatible with other projects from the community as well. This has two major disadvantages:

• It feels overcomplicated for the user as it contains features that are not relevant for the BlueBerry registry and it is not tailored to the workflow of the researcher.
• Adding new features to the UI is time-consuming as it needs to be compatible with other projects and requires approval from the vantage6 community.

For these two reasons, I decided it would be better to create a separate, dedicated UI for the BlueBerry registry. This way, I can tailor the workflow exactly as it should be and I don't have to consider other projects when adding new features.

To accelerate development, I used Streamlit. This framework brought the following advantages:

• It minimizes the need to write front-end code as the front-end code, as it is generated from Python code.
• It includes numerous built-in data science components like tables, graphs and controls.

However, it introduces an additional backend component, the one that renders the front end. The app's appearance and components can be customized, however the customization is very different from front-end frameworks like React or Angular.

This newly developed UI aims to better support the researcher's workflow. The first thing after logging in is to select the collaboration and optionally the study it wants to work with. Once the collaboration/study is selected, the user can view the online organizations within the collaboration or study. The user is at this point able to create sub selections of the organizations it wants to work with.

Collaboration & Study Selection	Node status
Users first need to select the collaboration and optionally the study they want to work with. Some metadata is shown about the selected collaboration and study.	Once the collaboration is selected, the user can view the online organizations. It is possible to create a sub selection of the organizations the user wants to work with.

Once the organizations are selected, the system checks which cohorts are available for the selected organizations. The UI then determines automatically which cohorts are ready for analysis, it validates that:

1. All the (online) organizations have the cohort available.
2. The minimal number of patients threshold is met at each organization.
3. All the organizations have the same variables and have the same value types for these variables.

By default, all the healthy cohorts are selected. The user can also make a sub selection of the cohorts it wants to work with. It is also possible to create a new cohort based on the ATLAS cohort definitions.

Cohort selection	Cohort creation
Users can select the cohorts they want to work with. By default, all the healthy cohorts are selected. In this case none of the cohorts are healthy.	Before the user can continue all the selected organizations need to have the cohort available. The user is able to select the cohorts and from there automatically select the organizations that passed the validation.

Once the cohorts have been selected the user can continue to the analytics part of the application. The first analytics that is available is the summary statistics. This gives an overview of all selected cohorts and its variables. It reports some basic statistics like missing, mean, standard deviation, etc.

The second analytics that is available is the crosstabulation. This is a useful tool to compare the distribution of two categorical variables. The user can select the variables it wants to compare and the crosstabulation is calculated for all selected cohorts.

The third analytics that is available is the Kaplan-Meier curve. This is can be used to compare the survival between cohorts. The dataset contains the survival time and the event indicator, so these are already preselected.

Summary statistics	Crosstabulation	Kaplan-Meier curve
Users can view the summary statistics of all selected cohorts. The summary statistics are calculated for all selected cohorts.	Users can compare the distribution of two variables. The crosstabulation is calculated for all selected cohorts.	Users can compare the survival of two cohorts. The Kaplan-Meier curve is calculated for all selected cohorts.

3   Future work

This project is still in development throughout 2025. There are still several features that need to be added to the system. The following features are planned:

1. The current algorithms need to be extended to support additional features like stratification.
2. Currently in development are some more advanced analytics like the Cox proportional hazard model and the propensity score matching.