Metadata-Version: 2.1
Name: pyhealth
Version: 0.0.6
Summary: A Python library for healthcare AI
Home-page: https://github.com/yzhao062/pyhealth
Author: Yue Zhao, Zhi Qiao, Cao Xiao
Author-email: zhaoy@cmu.edu
License: UNKNOWN
Download-URL: https://github.com/yzhao062/pyhealth/archive/master.zip
Description: A Python Library for Health Predictive Models (PyHealth)
        ========================================================
        
        
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        -----
        
        
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        -----
        
        
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           :align: center
        
        **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 <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**,
        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 <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 execution and deployment.
        
        **API Demo for LSTM on Phenotyping Prediction**\ :
        
        
           .. code-block:: python
        
        
               # 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 <https://github.com/yzhao062/pyhealth>`_ is under review at
        `JMLR <http://www.jmlr.org/>`_ (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 Sun, Jimeng},
              title   = {PyHealth: A Python Library for Health Predictive Models},
              year    = {2021},
            }
        
        or::
        
            Zhao, Y., Qiao, Z., Xiao, C., Glass, L. and Sun, J., 2021. PyHealth: A Python Library for Health Predictive Models.
        
        
        **Key Links and Resources**\ :
        
        
        * `View the latest codes on Github <https://github.com/yzhao062/pyhealth>`_
        * `Execute Interactive Jupyter Notebooks <https://mybinder.org/v2/gh/yzhao062/pyhealth/master>`_
        * `Check out the PyHealth paper <https://github.com/yzhao062/pyhealth>`_
        
        
        
        **Table of Contents**\ :
        
        
        * `Installation <#installation>`_
        * `API Cheatsheet & Reference <#api-cheatsheet--reference>`_
        * `Preprocessed Datasets & Implemented Algorithms <#preprocessed-datasets--implemented-algorithms>`_
        * `Quick Start for Data Processing <#quick-start-for-data-processing>`_
        * `Quick Start for Running Predictive Models <#quick-start-for-running-predictive-models>`_
        * `Algorithm Benchmark <#algorithm-benchmark>`_
        * `Blueprint & Development Plan <#blueprint--development-plan>`_
        * `How to Contribute <#how-to-contribute>`_
        * `Inclusion Criteria <#inclusion-criteria>`_
        
        ----
        
        
        Installation
        ^^^^^^^^^^^^
        
        It is recommended to use **pip** for installation. Please make sure
        **the latest version** is installed, as PyHealth is updated frequently:
        
        .. code-block:: bash
        
           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:
        
        .. code-block:: bash
        
           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.
        
        ----
        
        
        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** :
        
        **For sequence data**:
        
        ===================  ================  ========================================  ======================================================================================================  =====  ========================================
        Type                 Abbr              Class                                     Algorithm                                                                                               Year   Ref
        ===================  ================  ========================================  ======================================================================================================  =====  ========================================
        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]_
        Neural Networks      Dipole            pyhealth.models.sequence.dipole           Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks  2017   [#Ma2017Dipole]_
        Neural Networks      tLSTM             pyhealth.models.sequence.tlstm            Patient Subtyping via Time-Aware LSTM Networks                                                          2017   [#Baytas2017tLSTM]_
        Neural Networks      RAIM              pyhealth.models.sequence.raim             RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data                     2018   [#Xu2018RAIM]_
        Neural Networks      StageNet          pyhealth.models.sequence.stagenet         StageNet: Stage-Aware Neural Networks for Health Risk Prediction                                        2020   [#Gao2020StageNet]_
        ===================  ================  ========================================  ======================================================================================================  =====  ========================================
        
        
        **For image data**:
        
        ===================  ================  ========================================  ======================================================================================================  =====  ========================================
        Type                 Abbr              Class                                     Algorithm                                                                                               Year   Ref
        ===================  ================  ========================================  ======================================================================================================  =====  ========================================
        Neural Networks      CNN               pyhealth.models.sequence.basiccnn         Face recognition: A convolutional neural-network approach                                               1997   [#Lawrence1997Face]_
        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   [#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
        =======================  =======================  ======================================================================================================  =========================================================
        
        
        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" <https://github.com/yzhao062/pyhealth/blob/master/examples/data_generation/dataloader_mimic_demo_parallel.py>`_
        as an exmaple:
        
        #. First read in patient, admission, and event tables.
        
           .. code-block:: python
        
        
               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.
        
           .. code-block:: python
        
        
               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.
        
           .. code-block:: python
        
              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" <https://github.com/yzhao062/pyhealth/blob/master/examples/data_generation/dataloader_mimic_demo_parallel.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" <https://github.com/yzhao062/pyhealth/blob/master/examples/data_generation/generate_mortality_prediction_mimic_demo.py>`_
        
           .. code-block:: python
        
              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**.
        
        
        ----
        
        
        Quick Start for Running Predictive Models
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        
        **Note**: Before running examples, you need the datasets. Please download from the GitHub repository `"datasets" <https://github.com/yzhao062/PyHealth/tree/master/datasets>`_.
        You can either unzip them manually or running our script `"00_extract_data_run_before_learning.py" <https://github.com/yzhao062/pyhealth/blob/master/examples/learning_models/00_extract_data_run_before_learning.py>`_
        
        **Note**: `"examples/learning_models/example_sequence_gpu_mortality.py" <https://github.com/yzhao062/pyhealth/blob/master/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" <https://github.com/yzhao062/PyHealth/blob/master/examples/learning_models/example_text_diagnosis.py>`_, 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.
        
           .. code-block:: python
        
              # 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,.
        
           .. code-block:: python
        
              # 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.
        
           .. code-block:: python
        
              clf.load_model()
        
        #. Model training, parameters are learnt on the train datasets and verified on valid datasets
        
           .. code-block:: python
        
              clf.fit(cur_dataset.train, cur_dataset.valid)
        
        #. Model inferring, make prediction on the test datasets
        
           .. code-block:: python
        
              clf.inference(cur_dataset.test)
              pred_results = clf.get_results()
        
        
        #. Evaluation on the model. Multiple metrics are supported.
        
           .. code-block:: python
        
              # 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
        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
        
        
        ----
        
        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 *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*.
        
        .. [#Cho2014Learning] 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.
        
        .. [#Choi2016RETAIN] 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).
        
        .. [#Gao2020StageNet] 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).
        
        .. [#Hochreiter1997Long] Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. *Neural computation*, 9(8), pp.1735-1780.
        
        .. [#Lawrence1997Face] 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.
        
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Keywords: heathcare AI,healthcare,machine learning,data mining,neural networks,deep learning
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/x-rst
