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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Attend and Diagnose: Clinical Time Series Analysis Using Attention Models

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Huan Song

Arizona State University


Deepta Rajan

IBM Almaden Research Center


Jayaraman Thiagarajan

Lawrence Livermore National Labs


Andreas Spanias

Arizona State University


DOI:

10.1609/aaai.v32i1.11635


Abstract:

With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data. Powered by Recurrent Neural Network (RNN) architectures with Long Short-Term Memory (LSTM) units, deep neural networks have achieved state-of-the-art results in several clinical prediction tasks. Despite the success of RNN, its sequential nature prohibits parallelized computing, thus making it inefficient particularly when processing long sequences. Recently, architectures which are based solely on attention mechanisms have shown remarkable success in transduction tasks in NLP, while being computationally superior. In this paper, for the first time, we utilize attention models for clinical time-series modeling, thereby dispensing recurrence entirely. We develop the SAnD (Simply Attend and Diagnose) architecture, which employs a masked, self-attention mechanism, and uses positional encoding and dense interpolation strategies for incorporating temporal order. Furthermore, we develop a multi-task variant of SAnD to jointly infer models with multiple diagnosis tasks. Using the recent MIMIC-III benchmark datasets, we demonstrate that the proposed approach achieves state-of-the-art performance in all tasks, outperforming LSTM models and classical baselines with hand-engineered features.

Topics: AAAI

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HOW TO CITE:

Huan Song||Deepta Rajan||Jayaraman Thiagarajan||Andreas Spanias Attend and Diagnose: Clinical Time Series Analysis Using Attention Models Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Huan Song||Deepta Rajan||Jayaraman Thiagarajan||Andreas Spanias Attend and Diagnose: Clinical Time Series Analysis Using Attention Models AAAI 2018, .

Huan Song||Deepta Rajan||Jayaraman Thiagarajan||Andreas Spanias (2018). Attend and Diagnose: Clinical Time Series Analysis Using Attention Models. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Huan Song||Deepta Rajan||Jayaraman Thiagarajan||Andreas Spanias. Attend and Diagnose: Clinical Time Series Analysis Using Attention Models. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Huan Song||Deepta Rajan||Jayaraman Thiagarajan||Andreas Spanias. 2018. Attend and Diagnose: Clinical Time Series Analysis Using Attention Models. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Huan Song||Deepta Rajan||Jayaraman Thiagarajan||Andreas Spanias. (2018) "Attend and Diagnose: Clinical Time Series Analysis Using Attention Models", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Huan Song||Deepta Rajan||Jayaraman Thiagarajan||Andreas Spanias, "Attend and Diagnose: Clinical Time Series Analysis Using Attention Models", AAAI, p., 2018.

Huan Song||Deepta Rajan||Jayaraman Thiagarajan||Andreas Spanias. "Attend and Diagnose: Clinical Time Series Analysis Using Attention Models". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Huan Song||Deepta Rajan||Jayaraman Thiagarajan||Andreas Spanias. "Attend and Diagnose: Clinical Time Series Analysis Using Attention Models". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Huan Song||Deepta Rajan||Jayaraman Thiagarajan||Andreas Spanias. Attend and Diagnose: Clinical Time Series Analysis Using Attention Models. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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