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Erschienen in:
Common Misconceptions and Future Directions for AI in Medicine: A Physician-Data Scientist Perspective Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

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

verfasst von : Jeong Min Lee, Milos Hauskrecht

Erschienen in: Artificial Intelligence in Medicine

Verlag: Springer International Publishing

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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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Vorheriges Kapitel AI in Healthcare: Ethical and Privacy Challenges
Nächstes Kapitel Deep Learning Approach for Pathogen Detection Through Shotgun Metagenomics Sequence Classification
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Metadaten
Titel
Recent Context-Aware LSTM for Clinical Event Time-Series Prediction
verfasst von
Jeong Min Lee
Milos Hauskrecht
Copyright-Jahr
2019
Buch
Artificial Intelligence in Medicine

Print ISBN: 978-3-030-21641-2

Electronic ISBN: 978-3-030-21642-9

Copyright-Jahr: 2019

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