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

• URL: http://arxiv.org/abs/2204.13451v2
• Date: Mon, 2 May 2022 09:54:35 GMT
• Title: Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction
• Authors: Takayuki Katsuki, Kohei Miyaguchi, Akira Koseki, Toshiya Iwamori, Ryosuke Yanagiya, Atsushi Suzuki
• Abstract summary: We propose a novel data representation for EHR called cumulative stay-time representation (CTR) CTR 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.
• Score: 8.261597797345342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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