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https://github.com/dermatologist/fhiry
FHIR to pandas dataframe for data analytics, AI and ML!
https://github.com/dermatologist/fhiry
analytics data-analysis data-science ehealth fhir hacktoberfest jupyter-notebook pandas python pytorch tensorflow
Last synced: about 6 hours ago
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FHIR to pandas dataframe for data analytics, AI and ML!
- Host: GitHub
- URL: https://github.com/dermatologist/fhiry
- Owner: dermatologist
- License: mit
- Created: 2020-12-12T23:34:10.000Z (almost 4 years ago)
- Default Branch: develop
- Last Pushed: 2024-09-16T15:31:56.000Z (8 days ago)
- Last Synced: 2024-09-18T05:43:50.878Z (7 days ago)
- Topics: analytics, data-analysis, data-science, ehealth, fhir, hacktoberfest, jupyter-notebook, pandas, python, pytorch, tensorflow
- Language: Jupyter Notebook
- Homepage: https://nuchange.ca
- Size: 1 MB
- Stars: 32
- Watchers: 6
- Forks: 7
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Authors: AUTHORS.md
Awesome Lists containing this project
README
# :fire: fhiry - FHIR to pandas dataframe for data analytics, AI and ML
Virtual flattened view of *FHIR Bundle / ndjson / FHIR server / BigQuery!*
[](https://pypi.python.org/pypi/fhiry/)
:fire: **FHIRy** is a [python](https://www.python.org/) package to facilitate health data analytics and machine learning by converting a folder of [FHIR bundles](https://www.hl7.org/fhir/bundle.html)/ndjson from [bulk data export](https://hl7.org/fhir/uv/bulkdata/export/index.html) into a [pandas](https://pandas.pydata.org/docs/user_guide/index.html) 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.**
## UPDATE
Recently added support for **LLM based natural language queries** of FHIR bundles/ndjson using [llama-index](examples/llm_example.py). Please install the llm extras as follows. Please be cognizant of the privacy issues with publically hosted LLMs. Any feedback will be highly appreciated. [See usage](examples/llm_example.py)!
```
pip install fhiry[llm]
```
[See usage](examples/llm_example.py).
Test this with the [synthea sample](https://synthea.mitre.org/downloads) or the downloaded ndjson from the [SMART Bulk data server](https://bulk-data.smarthealthit.org/). Use the 'Discussions' tab above for feature requests.
:sparkles: Checkout [this template](https://github.com/dermatologist/kedro-multimodal) for Multimodal machine learning in healthcare!
## Installation
### Stable
```shell
pip install fhiry
```
### Latest dev version
```
pip install git+https://github.com/dermatologist/fhiry.git
```
## Usage
### 1. Import FHIR bundles (JSON) from folder to pandas dataframe
```python
import fhiry.parallel as fp
df = fp.process('/path/to/fhir/resources')
print(df.info())
```
Example source data set: [Synthea](https://synthea.mitre.org/downloads)
Jupyter notebook example: [`notebooks/synthea.ipynb`](notebooks/synthea.ipynb)
### 2. Import NDJSON from folder to pandas dataframe
```python
import fhiry.parallel as fp
df = fp.ndjson('/path/to/fhir/ndjson/files')
print(df.info())
```
Example source data set: [SMART Bulk Data Server](https://bulk-data.smarthealthit.org/) Export
Jupyter notebook example: [`notebooks/ndjson.ipynb`](notebooks/ndjson.ipynb)
### 3. Import FHIR Search results to pandas dataframe
Fetch and import resources from [FHIR Search API](https://www.hl7.org/fhir/search.html) results to pandas dataframe.
Documentation: [`fhir-search.md`](fhir-search.md)
#### Example: Import all conditions with a certain code from FHIR Server
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`](https://www.hl7.org/fhir/condition.html#search)) to a pandas dataframe:
```python
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](https://cloud.google.com/bigquery) [FHIR dataset](https://console.cloud.google.com/marketplace/details/mitre/synthea-fhir?q=synthea)
```python
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 file
```
## Filters
Pass 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" } }')
```
## Columns
* see df.columns
```
patientId
fullUrl
resource.resourceType
resource.id
resource.name
resource.telecom
resource.gender
...
...
...
```
### [Documentation](https://dermatologist.github.io/fhiry/)
## Give us a star ⭐️
If you find this project useful, give us a star. It helps others discover the project.
## Contributors
* [Bell Eapen](https://nuchange.ca) | [](https://twitter.com/beapen)
* [Markus Mandalka](https://github.com/Mandalka)
* PR welcome, please see [CONTRIBUTING.md](/CONTRIBUTING.md)
* [ using CC](https://alliancecan.ca/en/services/advanced-research-computing)