https://github.com/corazonlabs/lemonpie

A deep learning library for Electronic Health Record (EHR) data
https://github.com/corazonlabs/lemonpie

deep-learning ehr fastai fhir health-data healthcare nbdev pytorch synthea

Last synced: about 2 months ago
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A deep learning library for Electronic Health Record (EHR) data

• Host: GitHub
• URL: https://github.com/corazonlabs/lemonpie
• Owner: corazonlabs
• License: apache-2.0
• Created: 2020-11-26T00:27:38.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2023-04-12T05:18:48.000Z (about 3 years ago)
• Last Synced: 2025-12-16T18:53:22.544Z (5 months ago)
• Topics: deep-learning, ehr, fastai, fhir, health-data, healthcare, nbdev, pytorch, synthea
• Language: Jupyter Notebook
• Homepage: https://corazonlabs.github.io/lemonpie/
• Size: 18.6 MB
• Stars: 3
• Watchers: 1
• Forks: 1
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# lemonpie

> An open source deep learning library for Electronic Health Record (EHR) data.

In this initial release of the library ..

- it implements 2 deep learning models (an LSTM and a CNN) based on popular papers 

- that can be trained on synthetic EHR data, created using the open source [Synthea Patient Generator](https://github.com/synthetichealth/synthea/wiki)

- to predict conditions that are on the [CDC's list of top chronic diseases](https://www.cdc.gov/chronicdisease/about/costs/index.htm) that contribute most to healthcare costs

    - and is easily configurable to train on and predict any conditions in the dataset

The end goal is to 

- keep adding more model implementations 

- keep adding different publicly available datasets 

- and have a leaderboard to track which models and configurations work best on these datasets

## Install

With conda

- `conda install -c corazonlabs -c fastai -c conda-forge lemonpie`

With pip

- `pip install lemonpie`

Or ..

1. Git clone the repo

    - `https://github.com/corazonlabs/lemonpie.git`

2. Create a new conda env using the `environment.yml` file

    - `cd lemonpie`

    - `conda env create --name lemonpie --file environment.yml`

## How to use

1. Read through and then run the following **Quick Start** guides to get a general idea. 

    - if using the cloned repo, run these noteboooks 

        - `99_quick_walkthru.ipynb`

        - `99_running_exps.ipynb`

    - if using installed lib, just open a jupyter notebook and copy, paste & run cell-by-cell from these guides

        - [Quick Walkthrough](https://corazonlabs.github.io/lemonpie/quick_walkthru.html) 

        - [Running Experiments](https://corazonlabs.github.io/lemonpie/running_exps.html) 

2. Setup Synthea

    - Refer to [condensed instructions](https://corazonlabs.github.io/lemonpie/setup.html#Setup-Synthea)

    - Generate different datasets you like - e.g. 1K, 5K, 10K

3. Run experiments

    - Refer to **Detailed Docs** for customizations

## Roadmap

- **A leader-board to track which models and configurations work best on different publicly available datasets**.

- Callbacks, Mixed Precision, etc

    - Either upgrade the library to use fastai v2.

    - Or as a minimum, build functionality for fastai-style callbacks & [PyTorch AMP](https://pytorch.org/docs/stable/amp.html).

- More models

    - Pick some of the best EHR models out there and implement them.

    - **Ideas are welcome** - please use [Github discussions](https://github.com/corazonlabs/lemonpie/discussions) for suggestions.

- More datasets

    - eICU and MIMIC3 possibly.

    - **Ideas are welcome** -  [Github discussions](https://github.com/corazonlabs/lemonpie/discussions)

- NLP on clinical notes

    - Synthea does not have clinical notes, so this can only be done with other datasets.

- Predicting different conditions

    - Again different datasets will allow this - e.g. hospitalization data (length of stay, in-patient mortality), ER data, etc.

- Integraion with Experiment management tools like W&B, Comet, etc,.

## Known Issues & Limitations

1. `num_workers > 0` not working yet, under investigation

2. Test coverage

    - Need to write more tests for more comprehensive coverage

    

Look at [Issues](https://github.com/corazonlabs/lemonpie/issues) for details about these and others.

## References

This library is created using the awesome [nbdev](https://nbdev.fast.ai/)

Synthea [Synthetic Patient Population Simulator](https://github.com/synthetichealth/synthea)

> Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, Scott McLachlan, Synthea:An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 230–238, https://doi.org/10.1093/jamia/ocx079

LSTM Model *based on* this paper - [Scalable and accurate deep learning for electronic health records](http://arxiv.org/abs/1801.07860)

> Rajkomar, A., Oren, E., Chen, K. et al. Scalable and accurate deep learning with electronic health records. npj Digital Med 1, 18 (2018). https://doi.org/10.1038/s41746-018-0029-1

CNN Model *based* on this paper - [Deepr: A Convolutional Net for Medical Records](http://arxiv.org/abs/1607.07519)

> Nguyen, P., Tran, T., Wickramasinghe, N., & Venkatesh, S. (2017). $\mathtt {Deepr}$:A Convolutional Net for Medical Records. IEEE Journal of Biomedical and Health Informatics, 21, 22-30.