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ersw-project's Introduction

ERSW-project

This is the code repository for the article: An interpretable early dynamic sequential predictor for sepsis-induced coagulopathy progression in the real-world using AI, which is published in Frontiers in Medicine.

1. Here are some results of the article.

2. This is the code structure of ERSW-project-main.

  • Code structure

    ERSW-project-master

    data

    BIDMC set and XJTUMC set

    lib

    deep_learning_model.py
    machine_learning_model.py
    figure_plotting.py
    shap_plotting.py

    utils

    data_dividation.py
    get_sample.py
    merge_annotate.py
    pre_annotation.py

    main.py

3. The repository provides the code of our research reproduction as follows:

  • Install necessary Python dependencies, such as "torch", 'shap', and so on.
  • Acquire or generate the necessary Dataset which are used for analysis by the following ways.
    • BIDMC dataset were obtained from the MIMIC-III database, which can be downloaded from https://mimic.mit.edu/iii.
    • XJTUMC dataset were obtained from the Biobank of First Affiliated Hospital of Xi’an Jiaotong University, which is a restricted-access resource and is only available by submitting a request to the author and the institution. You can send your request to the email: [email protected]
  • Run main.py to deal with the data and develop the model for predicting the coagulopathy.

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