Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction
| Autor: | Katsuki, Takayuki, Miyaguchi, Kohei, Koseki, Akira, Iwamori, Toshiya, Yanagiya, Ryosuke, Suzuki, Atsushi |
|---|---|
| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.24963/ijcai.2022/536 |
| Popis: | 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. Comment: To be published in IJCAI-22 |
| Databáze: | arXiv |
| Externí odkaz: |
|