TY - GEN
T1 - PyHealth: A Deep Learning Toolkit for Healthcare Applications
AU - Yang, Chaoqi
AU - Wu, Zhenbang
AU - Jiang, Patrick
AU - Lin, Zhen
AU - Gao, Junyi
AU - Danek, Benjamin P.
AU - Sun, Jimeng
N1 - Funding Information:
This work was supported by NSF awards SCH-2205289, SCH-2014438, IIS-1838042, NIH award R01 1R01NS107291-01. Junyi Gao acknowledges the receipt of studentship awards from the Health Data Research UK-The Alan Turing Institute Wellcome PhD Programme in Health Data Science (Grant Ref: 218529/Z/19/Z).
Funding Information:
Junyi Gao is a Ph.D. student at the University of Edinburgh funded by the HDR UK-Turing Welcome Ph.D. Program. His research interests include spatio-temporal epidemiology prediction and individual-level clinical predictive modeling.
Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/8/4
Y1 - 2023/8/4
N2 - Deep learning (DL) has emerged as a promising tool in healthcare applications. However, the reproducibility of many studies in this field is limited by the lack of accessible code implementations and standard benchmarks. To address the issue, we create PyHealth, a comprehensive library to build, deploy, and validate DL pipelines for healthcare applications. PyHealth supports various data modalities, including electronic health records (EHRs), physiological signals, medical images, and clinical text. It offers various advanced DL models and maintains comprehensive medical knowledge systems. The library is designed to support both DL researchers and clinical data scientists. Upon the time of writing, PyHealth has received 633 stars, 130 forks, and 15k+ downloads in total on GitHub.This tutorial will provide an overview of PyHealth, present different modules, and showcase their functionality through hands-on demos. Participants can follow along and gain hands-on experience on the Google Colab platform during the session.
AB - Deep learning (DL) has emerged as a promising tool in healthcare applications. However, the reproducibility of many studies in this field is limited by the lack of accessible code implementations and standard benchmarks. To address the issue, we create PyHealth, a comprehensive library to build, deploy, and validate DL pipelines for healthcare applications. PyHealth supports various data modalities, including electronic health records (EHRs), physiological signals, medical images, and clinical text. It offers various advanced DL models and maintains comprehensive medical knowledge systems. The library is designed to support both DL researchers and clinical data scientists. Upon the time of writing, PyHealth has received 633 stars, 130 forks, and 15k+ downloads in total on GitHub.This tutorial will provide an overview of PyHealth, present different modules, and showcase their functionality through hands-on demos. Participants can follow along and gain hands-on experience on the Google Colab platform during the session.
U2 - 10.1145/3580305.3599178
DO - 10.1145/3580305.3599178
M3 - Conference contribution
SP - 5788
EP - 5789
BT - KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - ACM Association for Computing Machinery
ER -