Papers › EMIT- Event-Based Masked Auto Encoding for Irregular Time Series

EMIT- Event-Based Masked Auto Encoding for Irregular Time Series

25 Sep 2024arXiv:2409.16554archive 2025-07-28

Hrishikesh Patel, Ruihong Qiu, Adam Irwin, Shazia Sadiq, Sen Wang

Irregular time series, where data points are recorded at uneven intervals, are prevalent in healthcare settings, such as emergency wards where vital signs and laboratory results are captured at varying times. This variability, which reflects critical fluctuations in patient health, is essential for informed clinical decision-making. Existing self-supervised learning research on irregular time series often relies on generic pretext tasks like forecasting, which may not fully utilise the signal provided by irregular time series. There is a significant need for specialised pretext tasks designed for the characteristics of irregular time series to enhance model performance and robustness, especially in scenarios with limited data availability. This paper proposes a novel pretraining framework, EMIT, an event-based masking for irregular time series. EMIT focuses on masking-based reconstruction in the latent space, selecting masking points based on the rate of change in the data. This method preserves the natural variability and timing of measurements while enhancing the model's ability to process irregular intervals without losing essential information. Extensive experiments on the MIMIC-III and PhysioNet Challenge datasets demonstrate the superior performance of our event-based masking strategy. The code has been released at https://github.com/hrishi-ds/EMIT.

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batch_indices_to_mask_numba hrishi-ds/EMIT/src/event_masks/generate_event_masks_mimic_iii.py official repository ran MIT (permissive) · 50d80e69db8ed9fa · report
batch_indices_to_mask_numba_with_insignificant_masks hrishi-ds/EMIT/src/event_masks/generate_event_masks_mimic_iii.py official repository ran MIT (permissive) · 15cd4150d9fa7f1c · report
calculate_rate_of_change_numba hrishi-ds/EMIT/src/event_masks/generate_event_masks_mimic_iii.py official repository ran fingerprinted MIT (permissive) · 920cd3a05aae993a · report
extend_model hrishi-ds/EMIT/src/finetuning/finetune_mimic.py official repository ran MIT (permissive) · 1489d78f67b0582d · report
f hrishi-ds/EMIT/src/data_preprocessing/preprocess_mimic_iii.py official repository ran MIT (permissive) · 06ddfb40099e743b · report
f hrishi-ds/EMIT/src/data_preprocessing/preprocess_mimic_iii.py official repository ran MIT (permissive) · 2ff0290f2f537f28 · report
get_batches hrishi-ds/EMIT/src/pretraining/pretrain_mimic.py official repository ran MIT (permissive) · 1d91c0a1e3f11311 · report
inv_list hrishi-ds/EMIT/src/data_preprocessing/preprocess_mimic_iii.py official repository ran fingerprinted MIT (permissive) · 2f43552b3b5e5b82 · report
inv_list hrishi-ds/EMIT/src/data_preprocessing/preprocess_physionet_2012.py official repository ran fingerprinted MIT (permissive) · 0fe5d13698c6faf8 · report
load_and_generate_train_val_test_sets hrishi-ds/EMIT/src/finetuning/finetune_mimic.py official repository ran MIT (permissive) · ff295c09ba198ddc · report
load_and_generate_train_val_test_sets hrishi-ds/EMIT/src/finetuning/finetune_physionet.py official repository ran MIT (permissive) · 986be89dea6054c4 · report
load_data hrishi-ds/EMIT/src/pretraining/pretrain_mimic.py official repository ran MIT (permissive) · cbc9a2663cb2b73a · report
load_mask hrishi-ds/EMIT/src/pretraining/pretrain_mimic.py official repository ran MIT (permissive) · 19a72224a3e8b996 · report
pad hrishi-ds/EMIT/src/pretraining_data_preparation/get_pretraining_data_mimic_iii.py official repository ran fingerprinted MIT (permissive) · a753dc77394af824 · report
build_strats hrishi-ds/EMIT/src/model.py official repository unverified MIT (permissive) · f51fe6452652adec · report

Tasks

Decision MakingIrregular Time SeriesSelf-Supervised LearningTime Series

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