tempo-ql 0.1.1

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. Clone the repo, cd into it and run pip install ..

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

In demo.ipynb, we show how to use the query language using MIMIC-IV in OMOP format. You can run that notebook to explore how TempoQL enables simple, readable and precise queries on EHR data. We recommend installing JupyterLab (pip install jupyterlab), then running a JupyterLab instance (jupyter lab) in this repo directory, and opening and running demo.ipynb.

Example Usage

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()
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 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

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.