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yzhao062 committed Aug 18, 2020
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Expand Up @@ -6,4 +6,5 @@ v<0.0.2>, <08/06/2020> -- Add models and remove task parameter from model invoca
v<0.0.3>, <08/10/2020> -- Massive code refactoring.
v<0.0.3>, <08/11/2020> -- Add GPU support.
v<0.0.3>, <08/12/2020> -- Add support for image and clinical notes.
v<0.0.4>, <08/16/2020> -- Documentation update and binder setup.

42 changes: 25 additions & 17 deletions README.rst
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Expand Up @@ -14,6 +14,10 @@ Python Library for Healthcare AI (PyHealth)
:alt: Documentation status


.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/yzhao062/pyhealth/master
:alt: MyBinder

.. image:: https://img.shields.io/github/stars/yzhao062/pyhealth.svg
:target: https://github.com/yzhao062/pyhealth/stargazers
:alt: GitHub stars
Expand Down Expand Up @@ -72,16 +76,16 @@ Python Library for Healthcare AI (PyHealth)
:alt: PyHealth Logo
:align: center

**Development Status**: **As of 08/12/2020, PyHealth is under active development and in its alpha stage. Please follow, star, and fork to get the latest functions**!
**Development Status**: **As of 08/18/2020, PyHealth is under active development and in its alpha stage. Please follow, star, and fork to get the latest functions**!


**PyHealth** is a comprehensive and flexible **Python library** for **healthcare AI**, designed for both **ML researchers** and **medical practitioners**.
The library is proudly developed and maintained by researchers at `Carnegie Mellon University <https://www.cmu.edu/>`_, `IQVIA <https://www.iqvia.com/>`_, and `University of Illinois at Urbana-Champaign <https://illinois.edu/>`_.
The library is proudly developed and maintained by researchers from `Carnegie Mellon University <https://www.cmu.edu/>`_, `IQVIA <https://www.iqvia.com/>`_, and `University of Illinois at Urbana-Champaign <https://illinois.edu/>`_.
PyHealth makes many important healthcare tasks become accessible, such as **phenotyping prediction**, **mortality prediction**,
**ICU length stay forecasting**, etc. Running these prediction tasks with deep learning models can be as short as 10 lines of code.
and **ICU length stay forecasting**, etc. Running these prediction tasks with deep learning models can be as short as 10 lines of code in PyHealth.


PyHealth comes with three major modules: (i) *data preprocessing module*; (ii) *learning module*
**PyHealth comes with three major modules**: (i) *data preprocessing module*; (ii) *learning module*
and (iii) *evaluation module*. Typically, one can run the data prep module to prepare the data, then feed to the learning module for prediction, and finally assess
the result with the evaluation module.
Users can use the full system as mentioned or just selected modules based on the own need:
Expand All @@ -92,11 +96,11 @@ Users can use the full system as mentioned or just selected modules based on the

PyHealth is featured for:

* **Unified APIs, detailed documentation, and interactive examples** across various datasets and algorithms.
* **Unified APIs, detailed documentation, and interactive examples** across various types of datasets and algorithms.
* **Advanced models**\ , including **latest deep learning models** and **classical machine learning models**.
* **Wide coverage**, supporting **sequence data**, **image data**, and **text data** like clinical notes.
* **Optimized performance with JIT and parallelization** when possible, using `numba <https://github.com/numba/numba>`_ and `joblib <https://github.com/joblib/joblib>`_.
* **Customizable modules and flexible design**: each module may be turned on/off or totally replaced by custom functions. The trained models can be easily exported and reloaded for fast exexution and deployment.
* **Customizable modules and flexible design**: each module may be turned on/off or totally replaced by custom functions. The trained models can be easily exported and reloaded for fast execution and deployment.

**API Demo for LSTM on Phenotyping Prediction**\ :

Expand Down Expand Up @@ -240,29 +244,31 @@ Preprocessed Datasets & Implemented Algorithms

**(i) Preprocessed Datasets** (customized data preprocessing function is provided in the example folders):

=================== ================ ====================================================================================================== ====================================================== ===============================================================================================================
Type Abbr Description Processed Function Link
=================== ================ ====================================================================================================== ====================================================== ===============================================================================================================
EHR-ICU MIMIC III A relational database containing tables of data relating to patients who stayed within ICU. \\examples\\data_generation\\dataloader_mimic https://mimic.physionet.org/gettingstarted/overview/
EHR-ICU MIMIC_demo The MIMIC-III demo database is limited to 100 patients and excludes the noteevents table. \\examples\\data_generation\\dataloader_mimic_demo https://mimic.physionet.org/gettingstarted/demo/
EHU-Claim CMS DE-SynPUF: CMS 2008-2010 Data Entrepreneurs Synthetic Public Use File \\examples\\data_generation\\dataloader_cms https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/SynPUFs
=================== ================ ====================================================================================================== ====================================================== ===============================================================================================================
==================== ================ ====================================================================================================== ====================================================== ===============================================================================================================
Type Abbr Description Processed Function Link
==================== ================ ====================================================================================================== ====================================================== ===============================================================================================================
Sequence: EHR-ICU MIMIC III A relational database containing tables of data relating to patients who stayed within ICU. \\examples\\data_generation\\dataloader_mimic https://mimic.physionet.org/gettingstarted/overview/
Sequence: EHR-ICU MIMIC_demo The MIMIC-III demo database is limited to 100 patients and excludes the noteevents table. \\examples\\data_generation\\dataloader_mimic_demo https://mimic.physionet.org/gettingstarted/demo/
Sequence: EHU-Claim CMS DE-SynPUF: CMS 2008-2010 Data Entrepreneurs Synthetic Public Use File \\examples\\data_generation\\dataloader_cms https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/SynPUFs
Image: Chest X-ray Pediatric Pediatric Chest X-ray Pneumonia (Bacterial vs Viral vs Normal) Dataset N/A https://academictorrents.com/details/951f829a8eeb4d2839c4a535db95078a9175010b
==================== ================ ====================================================================================================== ====================================================== ===============================================================================================================

You may download the above datasets at the links. The structure of the generated datasets can be found in datasets folder:

* \\datasets\\cms\\x_data\\...csv
* \\datasets\\cms\\y_data\\phenotyping.csv
* \\datasets\\cms\\y_data\\mortality.csv


The processed datasets (X,y) should be put in x_data, y_data correspondingly, to be appropriately digested by deep learning models. We include some sample datasets under \\datasets folder.

**(ii) Machine Learning and Deep Learning Models** :

=================== ================ ======================================== ====================================================================================================== ===== ========================================
Type Abbr Algorithm Year Ref
Type Abbr Class Algorithm Year Ref
=================== ================ ======================================== ====================================================================================================== ===== ========================================
Classical Models LogisticReg pyhealth.models.sequence.lr Logistic Regression N/A
Classical Models XGBoost pyhealth.models.sequence.lr.xgboost XGBoost: A scalable tree boosting system 2016 [#Chen2016Xgboost]_
Classical Models RandomForest pyhealth.models.sequence.rf Random Forests 2000 [#Breiman2001Random]_
Classical Models XGBoost pyhealth.models.sequence.xgboost XGBoost: A scalable tree boosting system 2016 [#Chen2016Xgboost]_
Neural Networks LSTM pyhealth.models.sequence.lstm Long short-term memory 1997 [#Hochreiter1997Long]_
Neural Networks GRU pyhealth.models.sequence.gru Gated recurrent unit 2014 [#Cho2014Learning]_
Neural Networks RETAIN pyhealth.models.sequence.retain RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism 2016 [#Choi2016RETAIN]_
Expand Down Expand Up @@ -444,7 +450,9 @@ beyond EHR data, but also the images and clinical notes.
Reference
^^^^^^^^^

.. [#Baytas2017tLSTM] Baytas, I.M., Xiao, C., Zhang, X., Wang, F., Jain, A.K. and Zhou, J., 2017, August. Patient subtyping via time-aware lstm networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 65-74).
.. [#Baytas2017tLSTM] Baytas, I.M., Xiao, C., Zhang, X., Wang, F., Jain, A.K. and Zhou, J., 2017, August. Patient subtyping via time-aware lstm networks. In *KDD*.
.. [#Breiman2001Random] Breiman, L., 2001. Random forests. *Machine learning*, 45(1), pp.5-32.
.. [#Chen2016Xgboost] Chen, T. and Guestrin, C., 2016, August. Xgboost: A scalable tree boosting system. In *KDD*.
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6 changes: 3 additions & 3 deletions docs/api_cc.rst
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Expand Up @@ -3,10 +3,10 @@ API CheatSheet

Full API Reference: (https://pyhealth.readthedocs.io/en/latest/pyhealth.html). API cheatsheet for most learning models:

* **fit(X_train, X_valida)**\ : Fit a learning model.
* **inference(X)**\ : Predict on X using the fitted estimator.
* :func:`pyhealth.models.sequence._dlbase.fit` : Fit a learning model.
* :func:`pyhealth.models.sequence._dlbase.inference` : Predict on X using the fitted estimator.
* **evaluator(y, y^hat)**\ : Model evaluation.

Model load and reload:

* **load_model()**\ : Load the best model so far.
* :func:`pyhealth.models.sequence._dlbase.load_model` : Load the best model so far.
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