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Refer 'Detection_train.ipynb'.
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Change the directory based on your specific data directory.
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Train process includes:
(a) Window Interval Processing (ts_dataloader) (refer 'dataloader.py')
+ For 'ts_dataloader' function, you can select your specific input features ('lab_list', 'sign_list', 'dem_list'). + You can also select your specific task corresponding each label column.(Tasks colum's name includes: 'label' (normal or abnormal), 'is_detection', 'is_event', 'ev_w_dec'). + You can also select 'window_len' (history data using for prediction); 'stride' (Number of future timesteps we want to predict). + Finally, we have sequences process data for training DL models. Specifically for SiameseTS_model, It should includes 2 type of input features: 'x_t' (time based features); 'x_d' (non-time based features).
(b) Training
+ Change your specific training folder to save the trained models. + Select model (refer 'model.py') Example: 'model = SiamseTS_model(x_t.shape[1:], x_d.shape[1:], num_classes=num_classes)'
You can design your training strategy to improve the model's performance.
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Refer 'Detection_evaluation.ipynb'.
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Change the directory based on your specific data directory.
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Using ts_dataloader for test data. Input features list and task should be similar with the trained model.
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Get prediction and evaluation (refer 'eval.py')
Nguyen, T. N., Kim, S. H., Kho, B. G., & Yang, H. J. (2024). Multi-Gradient Siamese Temporal Model for the Prediction of Clinical Events in Rapid Response Systems. IEEE Intelligent Systems. DOI: 10.1109/MIS.2024.3408290