Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs

Ozmen, Ozgur; Klasky, Hilda; Omitaomu, Femi; Olama, Mohammed M.; Kuruganti, Teja; Pullum, Laura; Malviya, Addi Thakur; Ward, Merry; Scott, Jeanie; Laurio, Angela; Sauer, Brian; Drews, Frank; Nebeker, Jonathan R.

doi:10.1109/ICHI54592.2022.00129

Title: Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs

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Abstract

In highly configurable health information technology (HIT) systems, such as VistA of the Veterans Health Administration, the variations in how the system is used among different healthcare facilities and how the data are recorded can be significant. Despite the successful standardization of care efforts, some of these variations can be indicative of HIT hazards and demand further investigation. In this work, we implemented a recurrent neural network (RNN) architecture to learn clinical provider order sequences and their temporal dynamics while predicting the orders' terminal state. We demonstrate model performance and provide a use case for the model discerning novel event sequences. This model is proposed to find novel event sequences in an operational environment.

Authors:

^[1];

^[1]; Malviya, Addi Thakur ^[1]; Ward, Merry ^[2]; Scott, Jeanie ^[2]; Laurio, Angela ^[2]; Sauer, Brian ^[3]; Drews, Frank ^[2]; Nebeker, Jonathan R. ^[2]

ORNL
Department of Veterans Affairs
The University of Utah

Publication Date:: 2022-06-01

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1887719

DOE Contract Number:: AC05-00OR22725

Resource Type:: Conference

Resource Relation:: Conference: International Conference on Healthcare Informatics: ICHI'22 - Rochester, Minnesota, United States of America - 6/12/2022 4:00:00 AM-6/14/2022 8:00:00 AM

Country of Publication:: United States

Language:: English

Citation Formats


                    Ozmen, Ozgur, Klasky, Hilda, Omitaomu, Femi, Olama, Mohammed M., Kuruganti, Teja, Pullum, Laura, Malviya, Addi Thakur, Ward, Merry, Scott, Jeanie, Laurio, Angela, Sauer, Brian, Drews, Frank, and Nebeker, Jonathan R.. Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs.  United States: N. p., 2022. 
        Web.  doi:10.1109/ICHI54592.2022.00129.

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                    Ozmen, Ozgur, Klasky, Hilda, Omitaomu, Femi, Olama, Mohammed M., Kuruganti, Teja, Pullum, Laura, Malviya, Addi Thakur, Ward, Merry, Scott, Jeanie, Laurio, Angela, Sauer, Brian, Drews, Frank, & Nebeker, Jonathan R.. Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs.  United States.  https://doi.org/10.1109/ICHI54592.2022.00129

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                    Ozmen, Ozgur, Klasky, Hilda, Omitaomu, Femi, Olama, Mohammed M., Kuruganti, Teja, Pullum, Laura, Malviya, Addi Thakur, Ward, Merry, Scott, Jeanie, Laurio, Angela, Sauer, Brian, Drews, Frank, and Nebeker, Jonathan R.. 2022.  
        "Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs".  United States.  https://doi.org/10.1109/ICHI54592.2022.00129.  https://www.osti.gov/servlets/purl/1887719.

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@article{osti_1887719,

  title        = {Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs},

  author       = {Ozmen, Ozgur and Klasky, Hilda and Omitaomu, Femi and Olama, Mohammed M. and Kuruganti, Teja and Pullum, Laura and Malviya, Addi Thakur and Ward, Merry and Scott, Jeanie and Laurio, Angela and Sauer, Brian and Drews, Frank and Nebeker, Jonathan R.},

  abstractNote = {In highly configurable health information technology (HIT) systems, such as VistA of the Veterans Health Administration, the variations in how the system is used among different healthcare facilities and how the data are recorded can be significant. Despite the successful standardization of care efforts, some of these variations can be indicative of HIT hazards and demand further investigation. In this work, we implemented a recurrent neural network (RNN) architecture to learn clinical provider order sequences and their temporal dynamics while predicting the orders' terminal state. We demonstrate model performance and provide a use case for the model discerning novel event sequences. This model is proposed to find novel event sequences in an operational environment.},

  doi          = {10.1109/ICHI54592.2022.00129},

  url          = {https://www.osti.gov/biblio/1887719},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2022},

  month        = {6}

}

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