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Recent Context-Aware LSTM for Clinical Event Time-Series Prediction

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Artificial Intelligence in Medicine (AIME 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11526))

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Abstract

In this work, we propose a novel clinical event time-series model based on the long short-term memory architecture (LSTM) that can predict future event occurrences for a large number of different clinical events. Our model relies on two sources of information to predict future events. One source is derived from the set of recently observed clinical events. The other one is based on the hidden state space defined by the LSTM that aims to abstract past, more distant, patient information that is predictive of future events. We evaluate our proposed model on electronic health record (EHRs) data derived from MIMIC-III dataset. We show that the combination of the two sources of information implemented in our method leads to improved prediction performance compared to the models based on individual sources.

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Acknowledgement

The work in this paper was supported by NIH grant R01GM088224. The content of the paper is solely the responsibility of the authors and does not necessarily represent the official views of NIH.

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Authors and Affiliations

  1. University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Jeong Min Lee & Milos Hauskrecht

Authors
  1. Jeong Min Lee
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  2. Milos Hauskrecht
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Correspondence to Jeong Min Lee .

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Editors and Affiliations

  1. Universitat Rovira i Virgili, Tarragona, Spain

    David Riaño

  2. Poznan University of Technology, Poznan, Poland

    Szymon Wilk

  3. VU Amsterdam, Amsterdam, The Netherlands

    Annette ten Teije

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Lee, J.M., Hauskrecht, M. (2019). Recent Context-Aware LSTM for Clinical Event Time-Series Prediction. In: Riaño, D., Wilk, S., ten Teije, A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science(), vol 11526. Springer, Cham. https://doi.org/10.1007/978-3-030-21642-9_3

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  • DOI: https://doi.org/10.1007/978-3-030-21642-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21641-2

  • Online ISBN: 978-3-030-21642-9

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