pyhealth 0.0.5

A Python library for healthcare AI

Deployment & Documentation & Stats

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Build Status & Coverage & Maintainability & License

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Development Status: As of 11/09/2020, PyHealth is under active development and in its alpha stage. Please follow, star, and fork to get the latest functions!

PyHealth is a comprehensive 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{zhao2020pyhealth,
  author  = {Zhao, Yue and Qiao, Zhi and Xiao, Cao and Glass, Lucas and Hu, Xiyang and Sun, Jimeng},
  title   = {PyHealth: A Python Library for Healthcare AI},
  year    = {2020},
}

or:

Zhao, Y., Qiao, Z., Xiao, C., Glass, L., Hu, X and Sun, J., 2020. PyHealth: A Python Library for Healthcare AI.

Key Links and Resources:

• View the latest codes on Github

• Execute Interactive Jupyter Notebooks

• Check out the PyHealth paper

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

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Installation

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 features

Alternatively, 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.

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API Cheatsheet & Reference

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.

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 :

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	[8]
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	[9]
Neural Networks	StageNet	pyhealth.models.sequence.stagenet	StageNet: Stage-Aware Neural Networks for Health Risk Prediction	2020	[6]

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

Quick Start for Data Processing

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:

1. 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'))
   ...

2. 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))

3. 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.

1. 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.

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Quick Start for Running Predictive Models

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”

“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.

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.

1. 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()

2. 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)
   clf.fit(cur_dataset.train, cur_dataset.valid)

3. Load the best shot of the training, predict on the test datasets

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

4. 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)

Algorithm Benchmark

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

Blueprint & Development Plan

The long term goal of PyHealth is to become a comprehensive healthcare AI toolkit that supports beyond EHR data, but also the images and clinical notes.

• The compatibility and the support of OMOP format datasets

• Model persistence (save, load, and portability)

• The release of a benchmark paper with PyHealth

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Reference

[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]

Breiman, L., 2001. Random forests. Machine learning, 45(1), pp.5-32.

[3]

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]

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).

[9]

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).