@inproceedings{526f873b136f4d27ad6c296df96165cc,
title = "Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs",
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.",
keywords = "ehr, event sequences, feature engineering, lstm, rnn",
author = "Ozgur Ozmen and Klasky, {Hilda B.} and Omitaomu, {Olufemi A.} and Mohammed Olama and Teja Kuruganti and Laura Pullum and Malviya, {Addi T.} and Merry Ward and Scott, {Jeanie M.} and Angela Laurio and Brian Sauer and Frank Drews and Jonathan Nebeker",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 10th IEEE International Conference on Healthcare Informatics, ICHI 2022 ; Conference date: 11-06-2022 Through 14-06-2022",
year = "2022",
doi = "10.1109/ICHI54592.2022.00129",
language = "English",
series = "Proceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "639--643",
booktitle = "Proceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022",
}