Federated RDB Summary

Algorithm based on the Vantage 6 Federated Summary for relational databases following the OMOP CDM v6.0. It reports the following information from each node:

• Min
• Max
• Mean
• Pooled Standard Deviation
• Count
• Histogram
• Boxplot (reported individually for each node)

Additionally, it's also possible to evaluate the number of participants for a cohort.

The data privacy can be enhanced using environment variables that restrict the access to datasets that have a minimum amount of records. This is also applied to the results obtained from the different functions provided. See more below.

Node Setup

Make sure to set the database connection parameters as environment variables using the default variables for a postgres database (https://www.postgresql.org/docs/9.3/libpq-envars.html):

    application:
        ...
        algorithm_env:
            PGUSER: <user>
            PGPASSWORD: <password>
            PGDATABASE: <database>
            PGPORT: <port>
            PGHOST: <host>

Usage

from vantage6.client import Client

# Create, athenticate and setup client
client = Client("http://127.0.0.1", 5000, "")
client.authenticate("researcher@center.nl", "password")
client.setup_encryption(None)

# Define algorithm input
# The summary functions to be computed for each column will be selected in the following order:
# 1. the functions provided for a specific column
# 2. the functions provided for all columns
# 3. all functions will be computed
input_ = {
    "master": "true",
    "method":"master", 
    "args": [], 
    "kwargs": {
        "functions": [],
        "columns": [
            {
                "variable": "year_of_birth",
                "table": "PERSON",
                "functions": ["min", "max", "avg"]
            },
            {
                "variable": "381316",
                "table": "CONDITION_OCCURRENCE"
                "functions": ["count_null", "count", "count_discrete"],
            }
        ],
        # Optional argument to specify the organizations
        # "org_ids": [2, 3]
    }
}

# Send the task to the central server
task = client.post_task(
    name="summary",
    image="pmateus/v6-summary-omop:1.2.0",
    collaboration_id=1,
    input_= input_,
    organization_ids=[2]
)

# Retrieve the results
res = client.get_results(task_id=task.get("id"))

Histogram

The histogram function requires the bin width to be provided using the following variable BIN_WIDTH.

input_ = {
    "master": "true",
    "method":"master", 
    "args": [], 
    "kwargs": {
        "functions": [],
        "columns": [
            {
                "variable": "year_of_birth",
                "table": "PERSON",
                "functions": ["histogram"],
                "BIN_WIDTH": 4
            }
        ]
    }
}

Boxplot

The Boxplot function allows to specify the IQR used to determine the boundaries. By default this value is 1.5 but it can be changed using the following variable IQR_THRESHOLD.

input_ = {
    "master": "true",
    "method":"master", 
    "args": [], 
    "kwargs": {
        "functions": [],
        "columns": [
            {
                "variable": "year_of_birth",
                "table": "PERSON",
                "functions": ["boxplot"],
                "IQR_THRESHOLD": 2
            }
        ]
    }
}

Cohort

The cohort function allows to explore possible groups of participants based on a set of characteristics that can be set using the SQL operators:

input_ = {
    "master": "true",
    "method":"master", 
    "args": [], 
    "kwargs": {
        "cohort": {
            "definition": [
                {
                    "variable": "year_of_birth",
                    "operator": ">=",
                    "value": 1975,
                    "table": "Person",
                },
                {
                    "variable": "381316",
                    "operator": "=",
                    "value": 1,
                    "table": "condition_occurrence",
                },
            ],
        }
    }
}

Combined with the columns argument, it's possible to obtain summary statistics regarding the selected cohort:

input_ = {
    "master": "true",
    "method":"master", 
    "args": [], 
    "kwargs": {
        "cohort": {
            "definition": [
                {
                    "variable": "381316",
                    "operator": "=",
                    "value": 1,
                    "table": "condition_occurrence",
                }
            ],
        },
        "columns": [
            {
                "variable": "year_of_birth",
                "table": "PERSON",
                "functions": ["avg"]
            }
        ]
    }
}

In this example, we would be calculating the age average for the group of participants that has 1 as the value for the dead status.

Privacy

Federated learning distinguishes itself by bringing additional security and privacy by keeping the data at each center. Keeping that in mind, the following variables give control over the algorithm:

• TABLE_MINIMUM (default value = 10): minimum number of records available in the requested table to run the functions or the cohort functionality;
• COUNT_MINIMUM (default value = 5): minimum value allowed to be presented in the results when counting the number of records/outliers/...;
• BIN_WIDTH_MINIMUM (default value = 2): the minimum width for the histogram bins;

These environment variables can be used in the algorithm_env field when configuring the vantage6 node.

Test / Develop

You need to have Docker installed.

To Build (assuming you are in the project-directory):

docker build -t v6-summary-rdb .