On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series
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Date
2023
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Recent advances in deep learning architectures for sequence modeling have not fully transferred to tasks handling time-series from electronic health records. In particular, in problems related to the Intensive Care Unit (ICU), the state-of-the-art remains to tackle sequence classification in a tabular manner with tree-based methods. Recent findings in deep learning for tabular data are now surpassing these classical methods by better handling the severe heterogeneity of data input features. Given the similar level of feature heterogeneity exhibited by ICU time-series and motivated by these findings, we explore these novel methods impact on clinical sequence modeling tasks. By jointly using such advances in deep learning for tabular data, our primary objective is to underscore the importance of step-wise embeddings in time-series modeling, which remain unexplored in machine learning methods for clinical data. On a variety of clinically relevant tasks from two large-scale ICU datasets, MIMIC-III and HiRID, our work provides an exhaustive analysis of state-of-the-art methods for tabular time-series as time-step embedding models, showing overall performance improvement. In particular, we evidence the importance of feature grouping in clinical time-series, with significant performance gains when considering features within predefined semantic groups in the step-wise embedding module.
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Publication status
published
Book title
Proceedings of the 3rd Machine Learning for Health Symposium
Journal / series
Volume
225
Pages / Article No.
268 - 291
Publisher
PMLR
Event
3rd Machine Learning for Health Symposium (ML4H 2023)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Deep Learning; Healthcare; Time-Series; Step-wise Embeddings; Feature Groups
Organisational unit
09568 - Rätsch, Gunnar / Rätsch, Gunnar
09664 - Schölkopf, Bernhard / Schölkopf, Bernhard
02219 - ETH AI Center / ETH AI Center