Virtual flattened view of FHIR Bundle / ndjson / FHIR server / BigQuery!
🔥 FHIRy is a python package to facilitate health data analytics and machine learning by converting a folder of FHIR bundles/ndjson from bulk data export into a pandas data frame for analysis. You can import the dataframe into ML packages such as Tensorflow and PyTorch. FHIRy also supports FHIR server search and FHIR tables on BigQuery.
Test this with the synthea sample or the downloaded ndjson from the SMART Bulk data server. Use the 'Discussions' tab above for feature requests.
✨ Checkout this template for Multimodal machine learning in healthcare!
🔥 Checkout MedPrompt for Medical LLM prompts, including FHIR related prompts, such as text-to-FHIRQuery mapper!
pip install fhirypip install git+https://github.com/dermatologist/fhiry.git
import fhiry.parallel as fp
df = fp.process('/path/to/fhir/resources')
print(df.info())Example source data set: Synthea
Jupyter notebook example: notebooks/synthea.ipynb
import fhiry.parallel as fp
df = fp.ndjson('/path/to/fhir/ndjson/files')
print(df.info())Example source data set: SMART Bulk Data Server Export
Jupyter notebook example: notebooks/ndjson.ipynb
Fetch and import resources from FHIR Search API results to pandas dataframe.
Documentation: fhir-search.md
Fetch and import all condition resources with Snomed (Codesystem http://snomed.info/sct) Code 39065001 in the FHIR element Condition.code (resource type specific FHIR search parameter code) to a pandas dataframe:
from fhiry.fhirsearch import Fhirsearch
fs = Fhirsearch(fhir_base_url = "http://fhir-server:8080/fhir")
my_fhir_search_parameters = {
"code": "http://snomed.info/sct|39065001",
}
df = fs.search(resource_type = "Condition", search_parameters = my_fhir_search_parameters)
print(df.info())4. Import Google BigQuery FHIR dataset
from fhiry.bqsearch import BQsearch
bqs = BQsearch()
df = bqs.search("SELECT * FROM `bigquery-public-data.fhir_synthea.patient` LIMIT 20") # can be a path to .sql filePass a config json to any of the constructors:
- config_json can be a path to a json file.
df = fp.process('/path/to/fhir/resources', config_json='{ "REMOVE": ["resource.text.div"], "RENAME": { "resource.id": "id" } }')
fs = Fhirsearch(fhir_base_url = "http://fhir-server:8080/fhir", config_json = '{ "REMOVE": ["resource.text.div"], "RENAME": { "resource.id": "id" } }')
bqs = BQsearch('{ "REMOVE": ["resource.text.div"], "RENAME": { "resource.id": "id" } }')
- see df.columns
patientId
fullUrl
resource.resourceType
resource.id
resource.name
resource.telecom
resource.gender
...
...
...
If you find this project useful, give us a star. It helps others discover the project.
- Bell Eapen |
- Markus Mandalka
- PR welcome, please see CONTRIBUTING.md
using CC
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