CATNet: Cross-event attention-based time-aware network for medical event prediction.
Medical event prediction (MEP) is a fundamental task in the healthcare domain, which needs to predict medical events, including medications, diagnosis codes, laboratory tests, procedures, outcomes, and so on, according to historical medical records of patients. Many researchers have tried to build M...
| Publicado en: | Artificial Intelligence in Medicine Vol. 134 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160558816&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160558816 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Dec2022 vid: 134 pid: 1004 pub: Elsevier B.V. artinfo: ui: 160558816 160558816 NLM36462902 10.1016/j.artmed.2022.102440 NLM36462902 160558816 ppct: 1 formats: tig: atl: CATNet: Cross-event attention-based time-aware network for medical event prediction. aug: au: Liu, Sicen Wang, Xiaolong Xiang, Yang Xu, Hui Wang, Hui Tang, Buzhou affil: Department of Computer Science, Harbin Institue of Technology (Shenzhen), Shenzhen, China sug: subj: Software ab: Medical event prediction (MEP) is a fundamental task in the healthcare domain, which needs to predict medical events, including medications, diagnosis codes, laboratory tests, procedures, outcomes, and so on, according to historical medical records of patients. Many researchers have tried to build MEP models to overcome the challenges caused by the heterogeneous and irregular temporal characteristics of EHR data. However, most of them consider the heterogenous and temporal medical events separately and ignore the correlations among different types of medical events, especially relations between heterogeneous historical medical events and target medical events. In this paper, we propose a novel neural network based on attention mechanism called Cross-event Attention-based Time-aware Network (CATNet) for MEP. It is a time-aware, event-aware and task-adaptive method with the following advantages: 1) modeling heterogeneous information and temporal information in a unified way and considering irregular temporal characteristics locally and globally respectively, 2) taking full advantage of correlations among different types of events via cross-event attention. Experiments on two public datasets (MIMIC-III and eICU) show CATNet outperforms other state-of-the-art methods on various MEP tasks. The source code of CATNet is released at https://github.com/sherry6247/CATNet.git. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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