This repository contains a simple example to illustrate how to format a MATLAB entry for the George B. Moody PhysioNet Challenge 2022. 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.
For this example, we implemented a random forest classifier with several features. You can use a different classifier, features, and libraries for your entry. This simpple example is designed not not to perform well, so you should not use it as a baseline for your model's performance.
This code uses four main scripts, described below, to train and run a model for the 2022 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), 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, class_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; andclass_scores.csv(output; optional) is a collection of per-class scores for your model.
We will run the train_model and run_model scripts to train and run your model, so please check these scripts and the functions that they call.
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.
Please see the Challenge website for more details. Please post questions and concerns on the Challenge discussion forum.