Deployment & Documentation & Stats
Build Status & Coverage & Maintainability & License
Development Status: As of 01/04/2021, PyHealth is under active development and in its alpha stage. Please follow, star, and fork to get the latest functions!
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 model training and prediction, and finally assess the results with the evaluation module. Users can use the full system as mentioned or just selected modules based on their own needs:
- Deep learning researchers may directly use the processed data along with the proposed new models.
- Healthcare and Medical personnel, may leverage our data preprocessing module to convert the medical data to the format that machine learning models could digest, and then perform the inference tasks to get insights from the data. This package can support them in various health analytics tasks including disease detection, risk prediction, patient subtyping, health monitoring, etc.
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:
# 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:
Table of Contents:
- Installation
- API Cheatsheet & Reference
- Preprocessed Datasets & Implemented Algorithms
- Quick Start for Data Processing
- Quick Start for Running Predictive Models
- Algorithm Benchmark
- Blueprint & Development Plan
- How to Contribute
- Inclusion Criteria
It is recommended to use pip for installation. Please make sure the latest version is installed, as PyHealth is updated frequently:
pip install pyhealth # normal install
pip install --upgrade pyhealth # or update if needed
pip install --pre pyhealth # or include pre-release version for new featuresAlternatively, you could clone and run setup.py file:
git clone https://github.com/yzhao062/pyhealth.git
cd pyhealth
pip install .Required Dependencies:
- Python 3.5, 3.6, or 3.7
- combo>=0.0.8
- joblib
- numpy>=1.13
- numba>=0.35
- pandas>=0.25
- scipy>=0.20
- scikit_learn>=0.20
- tqdm
- torch (this should be installed manually)
- xgboost (this should be installed manually)
- xlrd >= 1.0.0
- zipfile36
- PyWavelets
- torch
- torchvision
- xgboost
Warning 1: PyHealth has multiple neural network based models, e.g., LSTM, which are implemented in PyTorch. However, PyHealth does NOT install these DL libraries for you. This reduces the risk of interfering with your local copies. If you want to use neural-net based models, please make sure PyTorch is installed. Similarly, models depending on xgboost, would NOT enforce xgboost installation by default.
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.
- evaluator(y, y^hat): Model evaluation.
Model load and reload:
- load_model(): Load the best model so far.
(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 | Random Forests | 2000 | [2] |
| Classical Models | XGBoost | pyhealth.models.sequence.xgboost | XGBoost: A scalable tree boosting system | 2016 | [3] |
| Neural Networks | LSTM | pyhealth.models.sequence.lstm | Long short-term memory | 1997 | [7] |
| Neural Networks | GRU | pyhealth.models.sequence.gru | Gated recurrent unit | 2014 | [4] |
| Neural Networks | RETAIN | pyhealth.models.sequence.retain | RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism | 2016 | [5] |
| Neural Networks | Dipole | pyhealth.models.sequence.dipole | Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks | 2017 | [9] |
| Neural Networks | tLSTM | pyhealth.models.sequence.tlstm | Patient Subtyping via Time-Aware LSTM Networks | 2017 | [1] |
| Neural Networks | RAIM | pyhealth.models.sequence.raim | RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data | 2018 | [10] |
| Neural Networks | StageNet | pyhealth.models.sequence.stagenet | StageNet: Stage-Aware Neural Networks for Health Risk Prediction | 2020 | [6] |
For image data:
| Type | Abbr | Class | Algorithm | Year | Ref |
|---|---|---|---|---|---|
| Neural Networks | CNN | pyhealth.models.sequence.basiccnn | Face recognition: A convolutional neural-network approach | 1997 | [8] |
| Neural Networks | Vggnet | pyhealth.models.sequence.typicalcnn | Very deep convolutional networks for large-scale image recognition | 2014 | |
| Neural Networks | Inception | pyhealth.models.sequence.typicalcnn | Rethinking the Inception Architecture for Computer Vision | ||
| Neural Networks | Resnet | pyhealth.models.sequence.typicalcnn | Deep Residual Learning for Image Recognition | ||
| Neural Networks | Resnext | pyhealth.models.sequence.typicalcnn | Aggregated Residual Transformations for Deep Neural Networks | ||
| Neural Networks | Densenet | pyhealth.models.sequence.typicalcnn | Densely Connected Convolutional Networks | ||
| Neural Networks | Mobilenet | pyhealth.models.sequence.typicalcnn | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications |
For ecg/egg data:
| Type | Abbr | Class | Algorithm | Year | Ref |
|---|---|---|---|---|---|
| Classical Models | RandomForest | pyhealth.models.ecg.rf | Random Forests | 2000 | [2] |
| Classical Models | XGBoost | pyhealth.models.ecg.xgboost | XGBoost: A scalable tree boosting system | 2016 | [3] |
| Neural Networks | BasicCNN1D | pyhealth.models.ecg.conv1d | Face recognition: A convolutional neural-network approach | 1997 | [8] |
| 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 |
We propose the idea of standard template, a formalized schema for healthcare datasets. Ideally, as long as the data is scanned as the template we defined, the downstream task processing and the use of ML models will be easy and standard. In short, it has the following structure: add a figure here. The dataloader for different datasets can be found in examples/data_generation. Using "examples/data_generation/dataloader_mimic_demo.py" as an exmaple:
First read in patient, admission, and event tables.
from pyhealth.utils.utility import read_csv_to_df patient_df = read_csv_to_df(os.path.join('data', 'mimic-iii-clinical-database-demo-1.4', 'PATIENTS.csv')) admission_df = read_csv_to_df(os.path.join('data', 'mimic-iii-clinical-database-demo-1.4', 'ADMISSIONS.csv')) ...
Then invoke the parallel program to parse the tables in n_jobs cores.
from pyhealth.data.base_mimic import parallel_parse_tables all_results = Parallel(n_jobs=n_jobs, max_nbytes=None, verbose=True)( delayed(parallel_parse_tables)( patient_df=patient_df, admission_df=admission_df, icu_df=icu_df, event_df=event_df, event_mapping_df=event_mapping_df, duration=duration, save_dir=save_dir) for i in range(n_jobs))
The processed sequential data will be saved in the prespecified directory.
with open(patient_data_loc, 'w') as outfile: json.dump(patient_data_list, outfile)
The provided examples in PyHealth mainly focus on scanning the data tables in the schema we have, and generate episode datasets. For instance, "examples/data_generation/dataloader_mimic_demo.py" demonstrates the basic procedure of processing MIMIC III demo datasets.
The next step is to generate episode/sequence data for mortality prediction. See "examples/data_generation/generate_mortality_prediction_mimic_demo.py"
with open(patient_data_loc, 'w') as outfile: json.dump(patient_data_list, outfile)
By this step, the dataset has been processed for generating X, y for phenotyping prediction. It is noted that the API across most datasets are similar. One may easily replicate this procedure by calling the data generation scripts in \examples\data_generation. You may also modify the parameters in the scripts to generate the customized datasets.
Preprocessed datasets are also available at \datasets\cms and \datasets\mimic.
Note: Before running examples, you need the datasets. Please download from the GitHub repository "datasets". You can either unzip them manually or running our script "00_extract_data_run_before_learning.py"
Note: "examples/learning_models/example_sequence_gpu_mortality.py" demonstrates the basic API of using GRU for mortality prediction. It is noted that the API across all other algorithms are consistent/similar.
Note: If you do not have the preprocessed datasets yet, download the \datasets folder (cms.zip and mimic.zip) from PyHealth repository, and run \examples\learning_models\extract_data_run_before_learning.py to prepare/unzip the datasets.
Note: For "certain examples", pretrained bert models are needed. You will need to download these pretrained models at:
- BERT+BioBERT: https://github.com/EmilyAlsentzer/clinicalBERT
- CharacterBERT+BioCharacterBERT: https://github.com/helboukkouri/character-bert
Please download, unzip, and save to ./auxiliary folder.
Setup the datasets. X and y should be in x_data and y_data, respectively.
# 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 a LSTM model, you may set up the parameters of the LSTM, e.g., n_epoch, learning_rate, etc,.
# 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)
Model loading, Load the saved model, default for 'best', maybe can personally set via '0', 'latest', etc.
clf.load_model()
Model training, parameters are learnt on the train datasets and verified on valid datasets
clf.fit(cur_dataset.train, cur_dataset.valid)
Model inferring, make prediction on the test datasets
clf.inference(cur_dataset.test) pred_results = clf.get_results()
Evaluation on the model. Multiple metrics are supported.
# evaluate the model from pyhealth.evaluation.evaluator import func r = func(pred_results['hat_y'], pred_results['y']) print(r)
The comparison among of implemented models will be made available later with a benchmark paper. TBA soon :)
The long term goal of PyHealth is to become a comprehensive healthcare AI toolkit that supports all sorts of data types and predictive tasks.
- The compatibility and the support of OMOP format datasets
- Model persistence (save, load, and portability)
- The release of a benchmark paper with PyHealth
| [1] | 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. |
| [2] | (1, 2) Breiman, L., 2001. Random forests. Machine learning, 45(1), pp.5-32. |
| [3] | (1, 2) Chen, T. and Guestrin, C., 2016, August. Xgboost: A scalable tree boosting system. In KDD. |
| [4] | Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. and Bengio, Y., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. |
| [5] | Choi, E., Bahadori, M.T., Sun, J., Kulas, J., Schuetz, A. and Stewart, W., 2016. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems (pp. 3504-3512). |
| [6] | Gao, J., Xiao, C., Wang, Y., Tang, W., Glass, L.M. and Sun, J., 2020, April. StageNet: Stage-Aware Neural Networks for Health Risk Prediction. In Proceedings of The Web Conference 2020 (pp. 530-540). |
| [7] | Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural computation, 9(8), pp.1735-1780. |
| [8] | (1, 2) Lawrence, S., Giles, C.L., Tsoi, A.C. and Back, A.D., 1997. Face recognition: A convolutional neural-network approach. IEEE transactions on neural networks, 8(1), pp.98-113. |
| [9] | Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T. and Gao, J., 2017, August. Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1903-1911). |
| [10] | Xu, Y., Biswal, S., Deshpande, S.R., Maher, K.O. and Sun, J., 2018, July. Raim: Recurrent attentive and intensive model of multimodal patient monitoring data. In Proceedings of the 24th ACM SIGKDD international conference on Knowledge Discovery & Data Mining (pp. 2565-2573). |