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https://github.com/dermatologist/pyomop
Python package for managing OHDSI clinical data models. Includes support for LLM based plain text queries!
https://github.com/dermatologist/pyomop
cdm clinical-trials datawarehouse hacktoberfest health-data-analysis health-informatics llm ohdsi python text-to-sql
Last synced: 6 days ago
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Python package for managing OHDSI clinical data models. Includes support for LLM based plain text queries!
- Host: GitHub
- URL: https://github.com/dermatologist/pyomop
- Owner: dermatologist
- License: gpl-3.0
- Created: 2020-05-02T16:09:27.000Z (over 4 years ago)
- Default Branch: develop
- Last Pushed: 2024-10-07T05:58:38.000Z (about 1 month ago)
- Last Synced: 2024-11-07T06:05:04.698Z (13 days ago)
- Topics: cdm, clinical-trials, datawarehouse, hacktoberfest, health-data-analysis, health-informatics, llm, ohdsi, python, text-to-sql
- Language: Python
- Homepage: https://nuchange.ca
- Size: 471 KB
- Stars: 37
- Watchers: 4
- Forks: 7
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Authors: AUTHORS.md
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README
# pyomop
[](https://www.python.org/)
[](https://pypi.python.org/pypi/pyomop/)
[](https://nuchange.ca)
* *Inspired by [@jbadger3's](https://github.com/jbadger3) [inspectomop](https://github.com/jbadger3/inspectomop)*
### [Documentation](https://dermatologist.github.io/pyomop/)
## UPDATE
Recently added support for **LLM based natural language queries** of OMOP CDM databases 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 pyomop[llm]
```
[See usage](examples/llm_example.py).
## Description
The [OHSDI](https://www.ohdsi.org/) OMOP Common Data Model allows for the systematic analysis of healthcare observational databases. This is a python library to use the CDM v6 compliant databases using SQLAlchemy as the ORM. **pyomop** also supports converting query results to a pandas dataframe (see below) for use in machine learning pipelines. See some useful [SQL Queries here.](https://github.com/OHDSI/QueryLibrary)
## Installation (stable)
```
pip install pyomop
```
## Installation (current)
* git clone this repository and:
```
pip install -e .
```
## Usage >= 4.0.0 (Async) Example
```
from pyomop import CdmEngineFactory, CdmVocabulary, CdmVector, Cohort, Vocabulary, metadata
from sqlalchemy.future import select
import datetime
import asyncio
async def main():
cdm = CdmEngineFactory() # Creates SQLite database by default
# Postgres example (db='mysql' also supported)
# cdm = CdmEngineFactory(db='pgsql', host='', port=5432,
# user='', pw='',
# name='', schema='cdm6')
engine = cdm.engine
# Create Tables if required
await cdm.init_models(metadata)
# Create vocabulary if required
vocab = CdmVocabulary(cdm)
# vocab.create_vocab('/path/to/csv/files') # Uncomment to load vocabulary csv files
# Add a cohort
async with cdm.session() as session:
async with session.begin():
session.add(Cohort(cohort_definition_id=2, subject_id=100,
cohort_end_date=datetime.datetime.now(),
cohort_start_date=datetime.datetime.now()))
await session.commit()
# Query the cohort
stmt = select(Cohort).where(Cohort.subject_id == 100)
result = await session.execute(stmt)
for row in result.scalars():
print(row)
assert row.subject_id == 100
# Query the cohort pattern 2
cohort = await session.get(Cohort, 1)
print(cohort)
assert cohort.subject_id == 100
# Convert result to a pandas dataframe
vec = CdmVector()
vec.result = result
print(vec.df.dtypes)
result = await vec.sql_df(cdm, 'TEST') # TEST is defined in sqldict.py
for row in result:
print(row)
result = await vec.sql_df(cdm, query='SELECT * from cohort')
for row in result:
print(row)
# Close session
await session.close()
await engine.dispose()
# Run the main function
asyncio.run(main())
```
## Usage <=3.2.0
```
from pyomop import CdmEngineFactory, CdmVocabulary, CdmVector, Cohort, Vocabulary, metadata
from sqlalchemy.sql import select
import datetime
cdm = CdmEngineFactory() # Creates SQLite database by default
# Postgres example (db='mysql' also supported)
# cdm = CdmEngineFactory(db='pgsql', host='', port=5432,
# user='', pw='',
# name='', schema='cdm6')
engine = cdm.engine
# Create Tables if required
metadata.create_all(engine)
# Create vocabulary if required
vocab = CdmVocabulary(cdm)
# vocab.create_vocab('/path/to/csv/files') # Uncomment to load vocabulary csv files
# Create a Cohort (SQLAlchemy as ORM)
session = cdm.session
session.add(Cohort(cohort_definition_id=2, subject_id=100,
cohort_end_date=datetime.datetime.now(),
cohort_start_date=datetime.datetime.now()))
session.commit()
result = session.query(Cohort).all()
for row in result:
print(row)
# Convert result to a pandas dataframe
vec = CdmVector()
vec.result = result
print(vec.df.dtypes)
# Execute a query and convert it to dataframe
vec.sql_df(cdm, 'TEST') # TEST is defined in sqldict.py
print(vec.df.dtypes) # vec.df is a pandas dataframe
# OR
vec.sql_df(cdm, query='SELECT * from cohort')
print(vec.df.dtypes) # vec.df is a pandas dataframe
```
## command-line usage
```
pyomop -help
```
## Other utils
**Want to convert FHIR to pandas data frame? Try [fhiry](https://github.com/dermatologist/fhiry)**
**Use the same functions in [.NET](https://github.com/dermatologist/omopcdm-dot-net) and [Golang](https://github.com/E-Health/gocdm)!**
### Support
* Postgres
* MySQL
* SqLite
* More to follow..
## 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)
* PRs welcome. See CONTRIBUTING.md