deid-risk

This project is intended to compute an estimated value of risk for a given database.

1. Pull meta data of the database  and create a dataset via joins
2. Generate the dataset with random selection of features
3. Compute risk via SQL using group by

Python environment

The following are the dependencies needed to run the code:

    pandas
    numpy
    pandas-gbq
    google-cloud-bigquery

Usage

*Generate The merged dataset

python risk.py create --i_dataset <in dataset|schema> --o_dataset <out dataset|schema> --table <name> --path <bigquery-key-file>  --key <patient-id-field-name> [--file ]

* Compute risk (marketer, prosecutor)

python risk.py compute --i_dataset <dataset> --table <name> --path <bigquery-key-file>  --key <patient-id-field-name> 

Limitations

- It works against bigquery for now
@TODO:    
    - Need to write a transport layer (database interface)
    - Support for referential integrity, so one table can be selected and a dataset derived given referential integrity
    - Add support for journalist risk