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Modeling asynchronous event sequences with RNNs.

Stephen Wu1, Sijia Liu1, Sunghwan Sohn1

  • 1Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.

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|June 9, 2018
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Summary
This summary is machine-generated.

This study introduces methods to model timestamped clinical event sequences, improving pediatric asthma classification by accounting for relative time in recurrent neural network models.

Keywords:
AsthmaDeep learningElectronic health recordsTemporal data

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Area of Science:

  • Computational methods
  • Biomedical informatics
  • Machine learning in healthcare

Background:

  • Traditional event sequence modeling often overlooks timestamped data, particularly in clinical settings like electronic health records (EHRs).
  • Clinical event data possesses inherent temporal properties such as synchronicity, evenness, and co-cardinality that are crucial for accurate analysis.
  • The temporal dynamics of chronic diseases like pediatric asthma necessitate models that explicitly handle relative time.

Purpose of the Study:

  • To define and incorporate temporal properties (synchronicity, evenness, co-cardinality) into event sequence modeling.
  • To develop and evaluate computational approaches for explicitly accounting for relative time in recurrent neural network (RNN) models.
  • To enhance the classification accuracy of pediatric asthma patient outcomes using temporally-aware models.

Main Methods:

  • Defined event sequences with properties of synchronicity, evenness, and co-cardinality.
  • Developed and applied methods to explicitly incorporate relative time into recurrent neural network (RNN) models.
  • Evaluated model performance on a pediatric asthma dataset, comparing outcomes with an inpatient intensive care setting.

Main Results:

  • Explicitly incorporating relative time into RNN models significantly improved the classification of pediatric asthma patient outcomes.
  • The developed methods demonstrated effectiveness in handling asynchronous, uneven, and multi-cardinal event sequences.
  • Performance gains were observed across classifications including no asthma, persistent asthma, long-term remission, and relapse.

Conclusions:

  • Accounting for relative time in event sequence modeling is critical for analyzing timestamped clinical data.
  • Temporally-sensitive RNN models offer improved accuracy for chronic disease management and patient stratification.
  • The proposed methods provide a robust framework for analyzing complex temporal patterns in healthcare data.