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Title: Improving Quality of Observational Streaming Medical Data by Using Long Short-Term Memory Networks (LSTMs)

Abstract

We present an exploration of the encoder-decoder structured Long Short-Term Memory Network (LSTM) as a detector of the anomalous missing observations in streaming medical data by using the difference between the LSTM-reconstructed and observed values as the anomaly detector. We experiment with time-series data from bedside monitoring devices from the available Medical Information Mart for Intensive Care Database (MIMIC). Our results show that not only encoder-decoder LSTM approach works well for detecting the difference between anomalous and normal missing observations in streaming medical data, but also has an imputation potential for the missing data.

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1463983
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Conference on Data Engineering (ICDEW 2018) - Paris, , France - 4/16/2018 8:00:00 AM-4/19/2018 8:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Bowie, Michael B., Begoli, Edmon, and Park, Byung H. Improving Quality of Observational Streaming Medical Data by Using Long Short-Term Memory Networks (LSTMs). United States: N. p., 2018. Web. doi:10.1109/ICDEW.2018.00015.
Bowie, Michael B., Begoli, Edmon, & Park, Byung H. Improving Quality of Observational Streaming Medical Data by Using Long Short-Term Memory Networks (LSTMs). United States. doi:10.1109/ICDEW.2018.00015.
Bowie, Michael B., Begoli, Edmon, and Park, Byung H. Sun . "Improving Quality of Observational Streaming Medical Data by Using Long Short-Term Memory Networks (LSTMs)". United States. doi:10.1109/ICDEW.2018.00015. https://www.osti.gov/servlets/purl/1463983.
@article{osti_1463983,
title = {Improving Quality of Observational Streaming Medical Data by Using Long Short-Term Memory Networks (LSTMs)},
author = {Bowie, Michael B. and Begoli, Edmon and Park, Byung H.},
abstractNote = {We present an exploration of the encoder-decoder structured Long Short-Term Memory Network (LSTM) as a detector of the anomalous missing observations in streaming medical data by using the difference between the LSTM-reconstructed and observed values as the anomaly detector. We experiment with time-series data from bedside monitoring devices from the available Medical Information Mart for Intensive Care Database (MIMIC). Our results show that not only encoder-decoder LSTM approach works well for detecting the difference between anomalous and normal missing observations in streaming medical data, but also has an imputation potential for the missing data.},
doi = {10.1109/ICDEW.2018.00015},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {4}
}

Conference:
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