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A cross-modal clinical prediction system for intensive care unit patient outcome

文献类型：期刊论文

作者Sun, Mengxuan1,2; Yang, Xuebing1; Niu, Jinghao1; Gu, Yifan1,2; Wang, Chutong1,2; Zhang, Wensheng1,3
刊名KNOWLEDGE-BASED SYSTEMS
出版日期2024-01-11
卷号283页码:16
ISSN号0950-7051
关键词Electronic health records Clinical outcome prediction Patient representation Cross-modal contrastive learning
DOI10.1016/j.knosys.2023.111160
通讯作者Yang, Xuebing(yangxuebing2013@ia.ac.cn) ; Zhang, Wensheng(zhangwenshengia@hotmail.com)
英文摘要Electronic Health Records (EHRs) are becoming an encyclopedia of clinical concepts, encompassing diagnoses, medications and clinical descriptions to describe patients' health conditions. The abundance of EHR data presents unprecedented opportunities for leveraging machine learning in clinical predictive tasks. However, the complex process of acquiring healthcare data in some settings like Intensive Care Unit (ICU) gives rise to various challenges in mining EHR, such as modality diversity, heterogeneity, data silo and data quality problems, etc. In this paper, to overcome the data heterogeneity and data quality issues, we propose a Code Text cross-modal Contrastive Learning (CTCL) framework specifically designed for predicting ICU patient outcomes. This framework leverages both structured (multi-view medical codes) and unstructured (clinical notes) EHR data to extract patient representations. To capture the complicated intra and inter interactions among medical codes, CTCL employs a multi-view graph convolution network to extract code encodings and a cross-view contrastive loss to regularize the model parameters. To enhance patient representations across different modalities, a cross-modal contrastive learning module is introduced. Experimental evaluations on the eICU-CRD database demonstrate that CTCL outperforms state-of-the-art competitors on the ICU mortality prediction task and in-hospital readmission prediction task. Furthermore, ablation studies and discussions demonstrate the effectiveness of each module of the framework.
资助项目Na-tional Key Research and Development Program of China[2021ZD0111005] ; Na-tional Key Research and Development Program of China[61976212] ; Na-tional Key Research and Development Program of China[62006139] ; Na-tional Key Research and Development Program of China[62203437] ; Na-tional Key Research and Development Program of China[61976213]
WOS研究方向Computer Science
语种英语
出版者ELSEVIER
WOS记录号WOS:001147289400001
资助机构Na-tional Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/54798]  
专题多模态人工智能系统全国重点实验室
通讯作者Yang, Xuebing; Zhang, Wensheng
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Guangzhou Univ, Guangzhou 510006, Peoples R China
推荐引用方式
GB/T 7714		 Sun, Mengxuan,Yang, Xuebing,Niu, Jinghao,et al. A cross-modal clinical prediction system for intensive care unit patient outcome[J]. KNOWLEDGE-BASED SYSTEMS,2024,283:16.
APA Sun, Mengxuan,Yang, Xuebing,Niu, Jinghao,Gu, Yifan,Wang, Chutong,&Zhang, Wensheng.(2024).A cross-modal clinical prediction system for intensive care unit patient outcome.KNOWLEDGE-BASED SYSTEMS,283,16.
MLA Sun, Mengxuan,et al."A cross-modal clinical prediction system for intensive care unit patient outcome".KNOWLEDGE-BASED SYSTEMS 283(2024):16.

入库方式： OAI收割

来源：自动化研究所

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