Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction

Takayuki Katsuki, Kohei Miyaguchi, Akira Koseki, Toshiya Iwamori, Ryosuke Yanagiya, Atsushi Suzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past. The common time-series representation is indirect in extracting such information from EHR because it focuses on detailed dependencies between values in successive observations, not cumulative information. We propose a novel data representation for EHR called cumulative stay-time representation (CTR), which directly models such cumulative health conditions. We derive a trainable construction of CTR based on neural networks that has the flexibility to fit the target data and scalability to handle high-dimensional EHR. Numerical experiments using synthetic and real-world datasets demonstrate that CTR alone achieves a high prediction performance, and it enhances the performance of existing models when combined with them.

Original languageEnglish
Title of host publicationProceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022
EditorsLuc De Raedt, Luc De Raedt
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3861-3867
Number of pages7
ISBN (Electronic)9781956792003
Publication statusPublished - 2022
Event31st International Joint Conference on Artificial Intelligence, IJCAI 2022 - Vienna, Austria
Duration: 23-07-202229-07-2022

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference31st International Joint Conference on Artificial Intelligence, IJCAI 2022
Country/TerritoryAustria
CityVienna
Period23-07-2229-07-22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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