This repository contains a simple example that illustrates how to format a MATLAB entry for the George B. Moody PhysioNet Challenge 2023. We recommend that you use this repository as a template for your entry. You can remove some of the code, reuse other code, and add new code to create your entry. You do not need to use the models, features, and/or libraries in this example for your approach, and we encourage a diversity of approaches for the Challenge.
For this example, we implemented a random forest model with several features. This simpple example is designed not not to perform well, so you should not use it as a baseline for your model's performance. You can try it by running the following commands on the Challenge training sets. These commands should take a few minutes or less to run from start to finish on a recent personal computer.
This code uses four main scripts, described below, to train and run a model for the Challenge.
You can train your model starting MATLAB and running
train_model(training_data, model)
where
training_data(input; required) is a folder with the training data files andmodel(output; required) is a folder for saving your model.
You can run your trained model by running
run_model(model, test_data, test_outputs)
where
model(input; required) is a folder for loading your model, andtest_data(input; required) is a folder with the validation or test data files (you can use the training data for debugging and cross-validation, but the validation and test data will not have labels and will have 12, 24, 48, or 72 hours of data), andtest_outputs(output; required) is a folder for saving your model outputs.
You can evaluate your model by pulling or downloading the evaluation code, installing Python, and running
evaluate_model(labels, outputs, scores.csv)
where
labels(input; required) is a folder with labels for the data, such as the training data on the PhysioNet webpage;outputs(input; required) is a folder containing files with your model's outputs for the data;scores.csv(output; optional) is a collection of scores for your model; and
Please edit the following script to add your training and testing code:
team_training_code.mis a script with functions for training your model.team_testing_code.mis a script with functions for running your model.
Please do not edit the following scripts. We will use the unedited versions of these scripts when running your code:
train_model.mis a script for training your model.run_model.mis a script for running your trained model.
These scripts must remain in the root path of your repository, but you can put other scripts and other files elsewhere in your repository.
To train and save your models, please edit the team_training_code function in the team_training_code.m script. Please do not edit the input or output arguments of the team_training_code function.
To load and run your trained model, please edit the load_model function in the load_model.m script and the team_testing_code function in the team_testing_code.m script. Please do not edit the input or output arguments of the functions of the load_model and team_testing_code functions.
This repository does not include code for evaluating your entry. Please see the evaluation code repository for code and instructions for evaluating your entry using the Challenge scoring metric.
This repository does not include code for preparing the validation and test sets. We will run your trained model on data without labels and with only 12, 24, 48, and 72 hours of recording data. Please see the Python example code repository for code and instructions for preparing the training data in the same way that we prepare the validation and test sets.
Please see the Challenge website for more details. Please post questions and concerns on the Challenge discussion forum.