tempo-ql 0.1.3

Toolkit and interactive widget for querying time-series healthcare data

TempoQL: Standardized Temporal Queries for ML in Healthcare

Quickstart

You may first want to create a conda environment to install packages in. Use a Python version between 3.10 and 3.14 for compatibility with TempoQL's dependencies. Then run:

pip install tempo-ql

We have had issues in the past running the JupyterLab widget with virtualenv - therefore we recommend using conda.

Online Examples

There are two examples on Google Colab to show you how TempoQL can be used, both involving the MIMIC-IV dataset but in different data formats:

• MIMIC-IV Demo Data in OMOP Common Data Model - uses only non-credentialed data
• MIMIC-IV Full Dataset on BigQuery - requires credentialed PhysioNet access and a Google Cloud Platform account. Follow the instructions on this page to get access.

Example Usage

The demo.ipynb and demo_mimiciv_full.ipynb notebooks in the repo (or the Colab examples above) shows how to use the query language using MIMIC-IV in OMOP format. You can run these to explore how TempoQL enables simple, readable and precise queries on EHR data.

You will need a dataset and a dataset specification to start using TempoQL. Then, you can import TempoQL and use it in your Python code like this:

from tempo_ql import QueryEngine, GenericDataset, formats

db_specification = formats.omop() # also available: mimiciv(), eicu()
sql_connection_string = "bigquery://my-project" # or "duckdb://my_local_db", etc.
dataset = GenericDataset(sql_connection_string, db_specification)

query_engine = QueryEngine(dataset)
# see demo.ipynb for further options, such as configuring a variable store

query_engine.list_data_elements(scope="Measurement") # returns a dataframe of Measurement concepts

query_engine.query("{Temperature Celsius; scope = Measurement}") # retrieves temperature measurements

You can access the interactive query authoring environment in a Jupyter notebook (or VSCode IPython notebook) like so:

query_engine.interactive(file_path=..., api_key=...)

Both file_path and api_key are optional. file_path allows you to read and write queries from a local JSON file, enabling you to persist the queries that you create in the interactive session. api_key can be a Gemini API key allowing you to use LLMs to author, update, explain, and debug queries.

Dev Notes

For local install: clone the repo, cd into it and run pip install -e ..

Running the dev server: Make sure you have NodeJS version 20 or later. cd into the client directory, run npm install, then npm run dev. Then in your call to QueryEngine.interactive, set dev=True. Now when you change the frontend source code, the widget will automatically update.

If the Vite dev server stops working after you make some changes (it may show a JavaScript error like 'failed to load model'), check that any imports of TypeScript types are prefixed with the word type.