PyHealth

Deep learning for healthcare: from raw EHR data to a trained, evaluated model in about 15 lines.

PyHealth is an open-source PyTorch toolkit for building, benchmarking and reproducing clinical AI models. It works with electronic health records (MIMIC-III, MIMIC-IV, eICU, OMOP), clinical text, biosignals (EEG, ECG) and medical images.

.. image:: https://img.shields.io/pypi/v/pyhealth.svg?color=brightgreen :target: https://pypi.org/project/pyhealth/ :alt: PyPI version

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Documentation <https://pyhealth.readthedocs.io/en/latest/>_ · Tutorials <https://pyhealth.readthedocs.io/en/latest/tutorials.html>_ · Paper (ICML 2026) <https://openreview.net/forum?id=gMLVFN9hl8>_ · Discord <https://discord.gg/mpb835EHaX>_ · Mailing list <https://docs.google.com/forms/d/e/1FAIpQLSfpJB5tdkI7BccTCReoszV9cyyX2rF99SgznzwlOepi5v-xLw/viewform?usp=header>_

.. image:: figure/poster.png :width: 810

Why PyHealth

• Healthcare-first data layer. Loaders for MIMIC-III, MIMIC-IV, eICU, OMOP-CDM, EHRShot, sleep and EEG datasets, chest X-rays and clinical notes. Includes medical code lookup and mapping across ICD, CCS, ATC, NDC and RxNorm.
• Ready-made clinical tasks. Mortality, readmission, length of stay, drug recommendation, medical coding, sleep staging and more. A custom task is a plain Python function.
• 33+ models, one interface. RNN, Transformer and Hugging Face models through RETAIN, SafeDrug, GAMENet, StageNet, ConCare and EHRMamba. Swap a model by changing one line.
• Reproducible evaluation. Patient-level splits, a trainer that keeps the best checkpoint, calibration metrics and bootstrap confidence intervals.
• Fast on large datasets. Polars and Dask processing with an on-disk cache, about 3x faster than pandas.

Quickstart

.. code-block:: sh

pip install pyhealth

This example trains and evaluates an in-hospital mortality model on synthetic MIMIC-III data. You don't need credentials to run it.

.. code-block:: python

from pyhealth.datasets import MIMIC3Dataset, get_dataloader, split_by_patient
from pyhealth.models import RNN
from pyhealth.tasks import MortalityPredictionMIMIC3
from pyhealth.trainer import Trainer

if __name__ == "__main__":  # needed in scripts; set_task uses worker processes
    dataset = MIMIC3Dataset(
        root="https://storage.googleapis.com/pyhealth/Synthetic_MIMIC-III/",
        tables=["DIAGNOSES_ICD", "PROCEDURES_ICD", "PRESCRIPTIONS"],
    )
    samples = dataset.set_task(MortalityPredictionMIMIC3())
    train, val, test = split_by_patient(samples, [0.8, 0.1, 0.1])

    model = RNN(dataset=samples)
    trainer = Trainer(model=model, metrics=["roc_auc", "pr_auc"])
    trainer.train(
        train_dataloader=get_dataloader(train, batch_size=32, shuffle=True),
        val_dataloader=get_dataloader(val, batch_size=32),
        epochs=3,
    )
    print(trainer.evaluate(get_dataloader(test, batch_size=32)))

Pointing root at your own MIMIC copy uses real data. You can also swap in MIMIC4Dataset, another task from pyhealth.tasks or another model from pyhealth.models, and the rest of the code stays the same. The tutorials <https://pyhealth.readthedocs.io/en/latest/tutorials.html>_ walk through each stage.

If PyHealth helps your work, please star the repo and cite the paper <#citing-pyhealth>_. Stars and citations help us keep the project funded and maintained.

1. Installation :rocket:

---

Python version: PyHealth 2.x requires Python 3.12 or 3.13 (>=3.12,<3.14).

Install the latest release from PyPI. This also installs PyTorch and the other deep learning dependencies:

.. code-block:: sh

pip install pyhealth

For the latest development version, or to contribute, install from source:

.. code-block:: sh

git clone https://github.com/sunlabuiuc/PyHealth.git
cd PyHealth
pip install -e .

Legacy version: PyHealth 1.1.6 supports Python 3.9+ but lacks the 2.x features:

.. code-block:: sh

pip install pyhealth==1.1.6

News: We keep adding influential papers and benchmarks to PyHealth. See the planned list <https://docs.google.com/spreadsheets/d/1PNMgDe-llOm1SM5ZyGLkmPysjC4wwaVblPLAHLxejTw/edit#gid=159213380>_. You're welcome to pick one and send a PR, or to suggest a paper.

2. Introduction :book:

---

pyhealth provides these functionalities (we are still enriching some modules):

.. image:: figure/overview.png :width: 770

You can use the following functions independently:

• Dataset: MIMIC-III, MIMIC-IV, eICU, OMOP-CDM, EHRShot, COVID19-CXR, SleepEDF, SHHS, ISRUC, customized EHR datasets, etc.
• Tasks: diagnosis-based drug recommendation, patient hospitalization and mortality prediction, readmission prediction, length of stay forecasting, sleep staging, etc.
• ML models: RNN, LSTM, GRU, Transformer, RETAIN, SafeDrug, GAMENet, MoleRec, AdaCare, ConCare, StageNet, GRASP, SparcNet, ContraWR, Deepr, TCN, Dr. Agent, etc.

Building a healthcare AI pipeline can be as short as 10 lines of code in PyHealth.

3. Build ML Pipelines :trophy:

---

All healthcare tasks in our package follow a five-stage pipeline:

.. image:: figure/five-stage-pipeline.png :width: 640

..

We try hard to make sure each stage is as separate as possible, so that people can customize their own pipeline by only using our data processing steps or the ML models.

Module 1: <pyhealth.datasets> """"""""""""""""""""""""""""""""""""

pyhealth.datasets provides a clean structure for the dataset, independent from the tasks. We support MIMIC-III, MIMIC-IV, eICU, OMOP-CDM, and more. The output (mimic3base) is a multi-level dictionary structure (see illustration below).

.. code-block:: python

from pyhealth.datasets import MIMIC3Dataset

mimic3base = MIMIC3Dataset(
    # root directory of the dataset
    root="https://storage.googleapis.com/pyhealth/Synthetic_MIMIC-III/",
    # raw CSV table name
    tables=["DIAGNOSES_ICD", "PROCEDURES_ICD", "PRESCRIPTIONS"],
)

.. image:: figure/structured-dataset.png :width: 400

..

Module 2: <pyhealth.tasks> """"""""""""""""""""""""""""""""""""

pyhealth.tasks defines how to process each patient's data into a set of samples for the tasks. In the package, we provide several task examples, such as drug recommendation, mortality prediction, and readmission prediction. It is easy to customize your own tasks following our template <https://colab.research.google.com/drive/1r7MYQR_5yCJGpK_9I9-A10HmpupZuIN-?usp=sharing>_.

.. code-block:: python

from pyhealth.tasks import ReadmissionPredictionMIMIC3

mimic3sample = mimic3base.set_task(ReadmissionPredictionMIMIC3())
mimic3sample[0] # show the information of the first sample

from pyhealth.datasets import split_by_patient, get_dataloader

train_ds, val_ds, test_ds = split_by_patient(mimic3sample, [0.8, 0.1, 0.1])
train_loader = get_dataloader(train_ds, batch_size=32, shuffle=True)
val_loader = get_dataloader(val_ds, batch_size=32, shuffle=False)
test_loader = get_dataloader(test_ds, batch_size=32, shuffle=False)

Module 3: <pyhealth.models> """"""""""""""""""""""""""""""""""""

pyhealth.models provides different ML models with very similar argument configs.

.. code-block:: python

from pyhealth.models import Transformer

model = Transformer(
    dataset=mimic3sample,
)

Module 4: <pyhealth.trainer> """"""""""""""""""""""""""""""""""""

pyhealth.trainer can specify training arguments, such as epochs, optimizer, learning rate, etc. The trainer will automatically save the best model and output the path in the end.

.. code-block:: python

from pyhealth.trainer import Trainer

trainer = Trainer(model=model)
trainer.train(
    train_dataloader=train_loader,
    val_dataloader=val_loader,
    epochs=50,
    monitor="pr_auc_samples",
)

Module 5: <pyhealth.metrics> """"""""""""""""""""""""""""""""""""

pyhealth.metrics provides several common evaluation metrics (refer to Doc <https://pyhealth.readthedocs.io/en/latest/api/metrics.html>_ and see what are available).

.. code-block:: python

# method 1
trainer.evaluate(test_loader)

# method 2
from pyhealth.metrics.binary import binary_metrics_fn

y_true, y_prob, loss = trainer.inference(test_loader)
binary_metrics_fn(y_true, y_prob, metrics=["pr_auc", "roc_auc"])

4. Medical Code Map :hospital:

pyhealth.codemap provides two core functionalities. This module can be used independently.

• For code ontology lookup within one medical coding system (e.g., name, category, sub-concept);

.. code-block:: python

from pyhealth.medcode import InnerMap

icd9cm = InnerMap.load("ICD9CM")
icd9cm.lookup("428.0")
# `Congestive heart failure, unspecified`
icd9cm.get_ancestors("428.0")
# ['428', '420-429.99', '390-459.99', '001-999.99']

atc = InnerMap.load("ATC")
atc.lookup("M01AE51")
# `ibuprofen, combinations`
atc.lookup("M01AE51", "drugbank_id")
# `DB01050`
atc.lookup("M01AE51", "description")
# Ibuprofen is a non-steroidal anti-inflammatory drug (NSAID) derived ...
atc.lookup("M01AE51", "indication")
# Ibuprofen is the most commonly used and prescribed NSAID. It is very common over the ...

• For code mapping between two coding systems (e.g., ICD9CM to CCSCM).

.. code-block:: python

from pyhealth.medcode import CrossMap

codemap = CrossMap.load("ICD9CM", "CCSCM")
codemap.map("428.0")
# ['108']

codemap = CrossMap.load("NDC", "RxNorm")
codemap.map("50580049698")
# ['209387']

codemap = CrossMap.load("NDC", "ATC")
codemap.map("50090539100")
# ['A10AC04', 'A10AD04', 'A10AB04']

5. Medical Code Tokenizer :speech_balloon:

pyhealth.tokenizer is used for transformations between string-based tokens and integer-based indices, based on the overall token space. We provide flexible functions to tokenize 1D, 2D and 3D lists. This module can be used independently.

.. code-block:: python

from pyhealth.tokenizer import Tokenizer

# Example: we use a list of ATC3 code as the token
token_space = ['A01A', 'A02A', 'A02B', 'A02X', 'A03A', 'A03B', 'A03C', 'A03D', \
        'A03F', 'A04A', 'A05A', 'A05B', 'A05C', 'A06A', 'A07A', 'A07B', 'A07C', \
        'A12B', 'A12C', 'A13A', 'A14A', 'A14B', 'A16A']
tokenizer = Tokenizer(tokens=token_space, special_tokens=["<pad>", "<unk>"])

# 2d encode
tokens = [['A03C', 'A03D', 'A03E', 'A03F'], ['A04A', 'B035', 'C129']]
indices = tokenizer.batch_encode_2d(tokens)
# [[8, 9, 10, 11], [12, 1, 1, 0]]

# 2d decode
indices = [[8, 9, 10, 11], [12, 1, 1, 0]]
tokens = tokenizer.batch_decode_2d(indices)
# [['A03C', 'A03D', 'A03E', 'A03F'], ['A04A', '<unk>', '<unk>']]

# 3d encode
tokens = [[['A03C', 'A03D', 'A03E', 'A03F'], ['A08A', 'A09A']], \
    [['A04A', 'B035', 'C129']]]
indices = tokenizer.batch_encode_3d(tokens)
# [[[8, 9, 10, 11], [24, 25, 0, 0]], [[12, 1, 1, 0], [0, 0, 0, 0]]]

# 3d decode
indices = [[[8, 9, 10, 11], [24, 25, 0, 0]], \
    [[12, 1, 1, 0], [0, 0, 0, 0]]]
tokens = tokenizer.batch_decode_3d(indices)
# [[['A03C', 'A03D', 'A03E', 'A03F'], ['A08A', 'A09A']], [['A04A', '<unk>', '<unk>']]]

..

6. Tutorials :teacher:

---

.. image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://pyhealth.readthedocs.io/en/latest/tutorials.html

..

We provide the following tutorials to help users get started with our pyhealth. Please bear with us as we update the documentation on how to use PyHealth 2.0.

Tutorial 0: Introduction to pyhealth.data <https://colab.research.google.com/drive/17nOzjIjKiAbC8bsntZ3h9xy2Vq4bKpuv?usp=sharing>_ [Video] <https://www.youtube.com/watch?v=Nk1itBoLOX8&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=2>__

Tutorial 1: Introduction to pyhealth.datasets <https://colab.research.google.com/drive/1vI_oljc7rU5ocsC26ITM7HUgD5SGZkFE?usp=sharing>_ [Video (PyHealth 1.16)] <https://www.youtube.com/watch?v=c1InKqFJbsI&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=3>__

Tutorial 2: Introduction to pyhealth.tasks <https://colab.research.google.com/drive/1QB0acnGb-wOuK53UNSgHxjCW74QeYjUl?usp=sharing>_ [Video (PyHealth 1.16)] <https://www.youtube.com/watch?v=CxESe1gYWU4&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=4>__

Tutorial 3: Introduction to pyhealth.models <https://colab.research.google.com/drive/1cUTSfFL1wLUXDBtJGTAWntolvcmxrDGo?usp=drive_link>_ [Video] <https://www.youtube.com/watch?v=fRc0ncbTgZA&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=6>__

Tutorial 4: Introduction to pyhealth.trainer <https://colab.research.google.com/drive/1up_SL0BxxHPO9pmjKQ98w1GbpiB7LySp?usp=drive_link>_ [Video] <https://www.youtube.com/watch?v=5Hyw3of5pO4&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=7>__

Tutorial 5: Introduction to pyhealth.metrics <https://colab.research.google.com/drive/1bO0h5BR62_kQ7zFOgzQmt5vb8jqJ0rV-?usp=drive_link>_ [Video] <https://www.youtube.com/watch?v=d-Kx_xCwre4&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=8>__

Tutorial 6: Introduction to pyhealth.tokenizer <https://colab.research.google.com/drive/1jhJ11MLUafhflQAz8HSrWiOEYlIhhvc_?usp=sharing>_ [Video] <https://www.youtube.com/watch?v=CeXJtf0lfs0&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=10>__

Tutorial 7: Introduction to pyhealth.medcode <https://colab.research.google.com/drive/1Tw1AUS53fotH1EYr4Abp7qYN3zDBeUbC?usp=drive_link>_ [Video] <https://www.youtube.com/watch?v=MmmfU6_xkYg&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=9>__

The following tutorials will help users build their own task pipelines.

Pipeline 1: Chest Xray Classification <https://colab.research.google.com/drive/18vK23gyI1LjWbTgkq4f99yDZA3A7Pxp9?usp=sharing>_ [Video] <https://www.youtube.com/watch?v=GGP3Dhfyisc&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=12>__

Pipeline 2: Medical Coding <https://colab.research.google.com/drive/1ThYP_5ng5xPQwscv5XztefkkoTruhjeK?usp=sharing>_

Pipeline 3: Medical Transcription Classification <https://colab.research.google.com/drive/1bjk_IArc2ZmXGR6u6Qzyf7kh70RdiY9c?usp=sharing>_

Pipeline 4: Mortality Prediction <https://colab.research.google.com/drive/1b9xRbxUz-HLzxsrvxdsdJ868ajGQCY6U?usp=sharing>_

Pipeline 5: Readmission Prediction <https://colab.research.google.com/drive/1h0pAymUlPQfkLFryI9QI37-HAW1tRxGZ?usp=sharing>_

We provide advanced tutorials for supporting various needs.

Advanced Tutorial 1: Fit your dataset into our pipeline <https://colab.research.google.com/drive/1UurxwAAov1bL_5OO3gQJ4gAa_paeJwJp?usp=sharing>_ [Video] <https://www.youtube.com/watch?v=xw2hGLEQ4Y0&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=13>__

Advanced Tutorial 2: Define your own healthcare task <https://colab.research.google.com/drive/1gK6zPXvfFGBM1uNaLP32BOKrnnJdqRq2?usp=sharing>_

Advanced Tutorial 3: Adopt customized model into pyhealth <https://colab.research.google.com/drive/1F_NJ90GC8_Eq-vKTf7Tyziew4gWjjKoH?usp=sharing>_ [Video] <https://www.youtube.com/watch?v=lADFlcmLtdE&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=14>__

Advanced Tutorial 4: Load your own processed data into pyhealth and try out our ML models <https://colab.research.google.com/drive/1ZRnKch2EyJLrI3G5AvDXVpeE2wwgBWfw?usp=sharing>_ [Video] <https://www.youtube.com/watch?v=xw2hGLEQ4Y0&list=PLR3CNIF8DDHJUl8RLhyOVpX_kT4bxulEV&index=13>__

7. Datasets :mountain_snow:

---

We provide the processing files for the following open EHR datasets:

=================== ======================================= ======================================== ======================================================================================================== MIMIC-III pyhealth.datasets.MIMIC3Dataset 2016 MIMIC-III Clinical Database <https://physionet.org/content/mimiciii/1.4//>_ MIMIC-IV pyhealth.datasets.MIMIC4Dataset 2020 MIMIC-IV Clinical Database <https://physionet.org/content/mimiciv/0.4/>_ eICU pyhealth.datasets.eICUDataset 2018 eICU Collaborative Research Database <https://eicu-crd.mit.edu//>_ OMOP pyhealth.datasets.OMOPDataset OMOP-CDM schema based dataset <https://www.ohdsi.org/data-standardization/the-common-data-model/>_ EHRShot pyhealth.datasets.EHRShotDataset 2023 Few-shot EHR benchmarking dataset <https://github.com/som-shahlab/ehrshot-benchmark>_ COVID19-CXR pyhealth.datasets.COVID19CXRDataset 2020 COVID-19 chest X-ray image dataset SleepEDF pyhealth.datasets.SleepEDFDataset 2018 Sleep-EDF dataset <https://physionet.org/content/sleep-edfx/1.0.0/>_ SHHS pyhealth.datasets.SHHSDataset 2016 Sleep Heart Health Study dataset <https://sleepdata.org/datasets/shhs>_ ISRUC pyhealth.datasets.ISRUCDataset 2016 ISRUC-SLEEP dataset <https://sleeptight.isr.uc.pt/?page_id=48>_ =================== ======================================= ======================================== ========================================================================================================

8. Machine/Deep Learning Models :airplane:

---

Deep Learning Models

================================== ====== ============================================================================================================ Model Year Key Innovation ================================== ====== ============================================================================================================ RETAIN 2016 Interpretable attention for clinical decisions GAMENet 2019 Memory networks for drug recommendation SafeDrug 2021 Molecular graphs for safe drug combinations MoleRec 2023 Substructure-aware drug recommendation AdaCare 2020 Scale-adaptive feature extraction ConCare 2020 Transformer-based patient modeling StageNet 2020 Disease progression stage modeling GRASP 2021 Graph neural networks for patient clustering MICRON 2021 Medication change prediction with recurrent residual networks ================================== ====== ============================================================================================================

Foundation Models

================================== ====== ============================================================================================================ Model Year Description ================================== ====== ============================================================================================================ Transformer 2017 Attention-based sequence modeling RNN/LSTM/GRU 2011 Recurrent neural networks for sequences CNN 1989 Convolutional networks for structured data TCN 2018 Temporal convolutional networks MLP 1986 Multi-layer perceptrons for tabular data ================================== ====== ============================================================================================================

Specialized Models

================================== ====== ============================================================================================================ Model Year Specialization ================================== ====== ============================================================================================================ ContraWR 2021 Biosignal analysis (EEG, ECG) SparcNet 2023 Seizure detection and sleep staging Deepr 2017 Electronic health records Dr. Agent 2020 Reinforcement learning for clinical decisions ================================== ====== ============================================================================================================

• Check the interactive map on benchmark EHR predictive tasks <https://pyhealth.readthedocs.io/en/latest/index.html#benchmark-on-healthcare-tasks>_.

9. Research Initiative :microscope:

---

The PyHealth Research Initiative is a year-round, open research program that brings together talented individuals from diverse backgrounds to conduct cutting-edge research in healthcare AI.

How to participate:

1. Join our Discord server <https://discord.gg/mpb835EHaX>_
2. Submit a high-quality PR to the PyHealth repository <https://github.com/sunlabuiuc/PyHealth>_
3. Check the documentation <https://pyhealth.readthedocs.io/en/latest/research_initiative.html>_ for more details

Recent research from the initiative has been published at venues including ML4H 2025 and other top conferences.

10. About Us :busts_in_silhouette:

---

We are the SunLab <http://sunlab.org/>_ healthcare research team at UIUC.

Current Maintainers:

• Zhenbang Wu <https://zzachw.github.io/>_ (Ph.D. Student @ UIUC)
• John Wu <https://jhnwu3.github.io/>_ (Ph.D. Student @ UIUC)
• Junyi Gao <http://aboutme.vixerunt.org/>_ (Ph.D. Student @ University of Edinburgh)
• Jimeng Sun <http://sunlab.org/>_ (Professor @ UIUC)

Get in Touch:

• Discord Community <https://discord.gg/mpb835EHaX>_ (fastest response)
• GitHub Issues <https://github.com/sunlabuiuc/PyHealth/issues>_
• Mailing List <https://docs.google.com/forms/d/e/1FAIpQLSfpJB5tdkI7BccTCReoszV9cyyX2rF99SgznzwlOepi5v-xLw/viewform?usp=header>_

.. _citing-pyhealth:

11. Citing PyHealth :handshake:

---

PyHealth 2.0 (2026):

Wu, John, Yongda Fan, Zhenbang Wu, Paul Landes, Eric Schrock, Sayeed Sajjad Razin, Arjun Chatterjee, Naveen Baskaran, Joshua Steier, Andrea Fitzpatrick, Bilal Arif, Rian Atri, Jathurshan Pradeepkumar, Siddhartha Laghuvarapu, Junyi Gao, Adam R. Cross, and Jimeng Sun. 2026. "PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning." In Proceedings of the 43rd International Conference on Machine Learning (ICML 2026).

.. code-block:: bibtex

@inproceedings{wu2026pyhealth,
    title={PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning},
    author={John Wu and Yongda Fan and Zhenbang Wu and Paul Landes and Eric Schrock and Sayeed Sajjad Razin and Arjun Chatterjee and Naveen Baskaran and Joshua Steier and Andrea Fitzpatrick and Bilal Arif and Rian Atri and Jathurshan Pradeepkumar and Siddhartha Laghuvarapu and Junyi Gao and Adam Richard Cross and Jimeng Sun},
    booktitle={Forty-third International Conference on Machine Learning},
    year={2026},
    url={https://openreview.net/forum?id=gMLVFN9hl8}
}

PyHealth 1.0 (2023):

Yang, Chaoqi, Zhenbang Wu, Patrick Jiang, Zhen Lin, Junyi Gao, Benjamin P. Danek, and Jimeng Sun. 2023. "PyHealth: A Deep Learning Toolkit for Healthcare Applications." In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 5788–89. KDD '23. New York, NY, USA: Association for Computing Machinery.

.. code-block:: bibtex

@inproceedings{pyhealth2023yang,
    author = {Yang, Chaoqi and Wu, Zhenbang and Jiang, Patrick and Lin, Zhen and Gao, Junyi and Danek, Benjamin and Sun, Jimeng},
    title = {{PyHealth}: A Deep Learning Toolkit for Healthcare Predictive Modeling},
    url = {https://github.com/sunlabuiuc/PyHealth},
    booktitle = {Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2023},
    year = {2023}
}