Welcome to PyHealth’s documentation!¶

[Oct 2022] We will release a brand-new version of PyHealth in the next few weeks. It will include more EHR datasets, health-related tasks, and state-of-the-art models. Please stay tuned!

Deployment & Documentation & Stats

Build Status & Coverage & Maintainability & License

PyHealth is a comprehensive Python package for healthcare AI, designed for both ML researchers and healthcare and medical practitioners. PyHealth accepts diverse healthcare data such as longitudinal electronic health records (EHRs), continuous signials (ECG, EEG), and clinical notes (to be added), and supports various predictive modeling methods using deep learning and other advanced machine learning algorithms published in the literature.

The library is proudly developed and maintained by researchers from Carnegie Mellon University, IQVIA, and University of Illinois at Urbana-Champaign. PyHealth makes many important healthcare tasks become accessible, such as phenotyping prediction, mortality prediction, 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 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:

Deep learning researchers may directly use the processed data along with the proposed new models.
Medical personnel, may leverage our data preprocessing module to convert the medical data to the format that learning models could digest, and then perform the inference tasks to get insights from the data.

PyHealth is featured for:

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, series data and text data like clinical notes.
Optimized performance with JIT and parallelization when possible, using numba and 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 execution and deployment.

API Demo for LSTM on Phenotyping Prediction with GPU:

# load pre-processed CMS dataset
from pyhealth.data.expdata_generator import sequencedata as expdata_generator

expdata_id = '2020.0810.data.mortality.mimic'
cur_dataset = expdata_generator(exp_id=exp_id)
cur_dataset.get_exp_data(sel_task='mortality', )
cur_dataset.load_exp_data()

# initialize the model for training
from pyhealth.models.sequence.lstm import LSTM
# enable GPU
expmodel_id = 'test.model.lstm.0001'
clf = LSTM(expmodel_id=expmodel_id, n_batchsize=20, use_gpu=True, n_epoch=100)
clf.fit(cur_dataset.train, cur_dataset.valid)

# load the best model for inference
clf.load_model()
clf.inference(cur_dataset.test)
pred_results = clf.get_results()

# evaluate the model
from pyhealth.evaluation.evaluator import func
r = func(pred_results['hat_y'], pred_results['y'])
print(r)

Citing PyHealth:

PyHealth paper is under review at JMLR (machine learning open-source software track). If you use PyHealth in a scientific publication, we would appreciate citations to the following paper:

@article{zhao2021pyhealth,
  title={PyHealth: A Python Library for Health Predictive Models},
  author={Zhao, Yue and Qiao, Zhi and Xiao, Cao and Glass, Lucas and Sun, Jimeng},
  journal={arXiv preprint arXiv:2101.04209},
  year={2021}
}

or:

Zhao, Y., Qiao, Z., Xiao, C., Glass, L. and Sun, J., 2021. PyHealth: A Python Library for Health Predictive Models. arXiv preprint arXiv:2101.04209.

Key Links and Resources:

Preprocessed Datasets & Implemented Algorithms¶

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

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
Series: ECG	PhysioNet	AF Classification from a short single lead ECG recording Dataset.	N/A	https://archive.physionet.org/challenge/2017/#challenge-data

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 :

For sequence data:

Type	Abbr	Class	Algorithm	Year	Ref
Classical Models	RandomForest	`pyhealth.models.sequence.rf.RandomForest`	Random forests	2000	[ABre01]
Classical Models	XGBoost	`pyhealth.models.sequence.xgboost.XGBoost`	XGBoost: A scalable tree boosting system	2016	[#Chen2016Xgboost]_
Neural Networks	LSTM	`pyhealth.models.sequence.lstm.LSTM`	Long short-term memory	1997	[#Hochreiter1997Long]_
Neural Networks	GRU	`pyhealth.models.sequence.gru.GRU`	Gated recurrent unit	2014	[#Cho2014Learning]_
Neural Networks	RETAIN	`pyhealth.models.sequence.retain.RetainAttention`	RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism	2016	[#Choi2016RETAIN]_
Neural Networks	Dipole	`pyhealth.models.sequence.dipole.Dipole`	Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks	2017	[#Ma2017Dipole]_
Neural Networks	tLSTM	`pyhealth.models.sequence.tlstm.tLSTM`	Patient Subtyping via Time-Aware LSTM Networks	2017	[#Baytas2017tLSTM]_
Neural Networks	RAIM	`pyhealth.models.sequence.raim.RAIM`	RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data	2018	[#Xu2018RAIM]_
Neural Networks	StageNet	`pyhealth.models.sequence.stagenet.StageNet`	StageNet: Stage-Aware Neural Networks for Health Risk Prediction	2020	[#Gao2020StageNet]_

For image data:

For ecg/egg data:

Type	Abbr	Class	Algorithm	Year	Ref
Classical Models	RandomForest	pyhealth.models.ecg.rf	Random Forests	2000	[#Breiman2001Random]_
Classical Models	XGBoost	pyhealth.models.ecg.xgboost	XGBoost: A scalable tree boosting system	2016	[#Chen2016Xgboost]_
Neural Networks	BasicCNN1D	pyhealth.models.ecg.conv1d	Face recognition: A convolutional neural-network approach	1997	[#Lawrence1997Face]_
Neural Networks	DBLSTM-WS	pyhealth.models.ecg.dblstm_ws	A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification	2018
Neural Networks	DeepRes1D	pyhealth.models.ecg.deepres1d	Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram	2019
Neural Networks	AE+BiLSTM	pyhealth.models.ecg.sdaelstm	Automatic Classification of CAD ECG Signals With SDAE and Bidirectional Long Short-Term Network	2019
Neural Networks	KRCRnet	pyhealth.models.ecg.rcrnet	K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection	2019
Neural Networks	MINA	pyhealth.models.ecg.mina	MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals	2019

Examples of running ML and DL models can be found below, or directly at \examples\learning_examples\

(iii) Evaluation Metrics :

Type	Abbr	Metric	Method
Binary Classification	average_precision_score	Compute micro/macro average precision (AP) from prediction scores	pyhealth.evaluation.xxx.get_avg_results
Binary Classification	roc_auc_score	Compute micro/macro ROC AUC score from prediction scores	pyhealth.evaluation.xxx.get_avg_results
Binary Classification	recall, precision, f1	Get recall, precision, and f1 values	pyhealth.evaluation.xxx.get_predict_results
Multi Classification	To be done here

(iv) Supported Tasks:

Type	Abbr	Description	Method
Multi-classification	phenotyping	Predict the diagnosis code of a patient based on other information, e.g., procedures	\examples\data_generation\generate_phenotyping_xxx.py
Binary Classification	mortality prediction	Predict whether a patient may pass away during the hospital	\examples\data_generation\generate_mortality_xxx.py
Regression	ICU stay length pred	Forecast the length of an ICU stay	\examples\data_generation\generate_icu_length_xxx.py

Algorithm Benchmark¶

The comparison among of implemented models will be made available later with a benchmark paper. TBA soon :)

References

ABre01: Leo Breiman. Random forests. Machine learning, 45(1):5–32, 2001.

Welcome to PyHealth’s documentation!¶

Preprocessed Datasets & Implemented Algorithms¶

Algorithm Benchmark¶

Indices and tables¶