{"type":"ScholarlyArticle","title":"VecoCare: Visit Sequences-Clinical Notes Joint Learning for Diagnosis Prediction in Healthcare Data","authors":[{"name":"Yongxin Xu","slug":"yongxin-xu","given":"Yongxin","family":"Xu","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; School of Computer Science and School of Software & Microelectronics , Peking University , Beijing , China"},{"name":"Kai Yang","slug":"kai-yang","given":"Kai","family":"Yang","affiliation":"Zhongguancun Laboratory , Beijing , China"},{"name":"Chaohe Zhang","slug":"chaohe-zhang","given":"Chaohe","family":"Zhang","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; School of Computer Science and School of Software & Microelectronics , Peking University , Beijing , China"},{"name":"Peinie Zou","slug":"peinie-zou","given":"Peinie","family":"Zou","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; School of Computer Science and School of Software & Microelectronics , Peking University , Beijing , China"},{"name":"Zhiyuan Wang","slug":"zhiyuan-wang","given":"Zhiyuan","family":"Wang","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; School of Computer Science and School of Software & Microelectronics , Peking University , Beijing , China"},{"name":"Hongxin Ding","slug":"hongxin-ding","given":"Hongxin","family":"Ding","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; School of Computer Science and School of Software & Microelectronics , Peking University , Beijing , China"},{"name":"Junfeng Zhao","slug":"junfeng-zhao","given":"Junfeng","family":"Zhao","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; School of Computer Science and School of Software & Microelectronics , Peking University , Beijing , China"},{"name":"Yasha Wang","slug":"yasha-wang","given":"Yasha","family":"Wang","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; National Engineering Research Center For Software Engineering , Peking University , Beijing , China"},{"name":"Bing Xie","slug":"bing-xie","given":"Bing","family":"Xie","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; School of Computer Science and School of Software & Microelectronics , Peking University , Beijing , China"}],"abstract":"Due to the insufficiency of electronic health records (EHR) data utilized in practical diagnosis prediction scenarios, most works are devoted to learning powerful patient representations either from structured EHR data (e.g., temporal medical events, lab test results, etc.) or unstructured data (e.g., clinical notes, etc.). However, synthesizing rich information from both of them still needs to be explored. Firstly, the heterogeneous semantic biases across them heavily hinder the synthesis of representation spaces, which is critical for diagnosis prediction. Secondly, the intermingled quality of partial clinical notes leads to inadequate representations of to-be-predicted patients. Thirdly, typical attention mechanisms mainly focus on aggregating information from similar patients, ignoring important auxiliary information from others. To tackle these challenges, we propose a novel visit sequences-clinical notes joint learning approach, dubbed VecoCare. It performs a Gromov-Wasserstein Distance (GWD)-based contrastive learning task and an adaptive masked language model task in a sequential pre-training manner to reduce heterogeneous semantic biases. After pre-training, VecoCare further aggregates information from both similar and dissimilar patients through a dual-channel retrieval mechanism. We conduct diagnosis prediction experiments on two real-world datasets, which indicates that VecoCare outperforms state-of-the-art approaches. Moreover, the findings discovered by VecoCare are consistent with the medical researches.","doi":"10.24963/ijcai.2023/547","url":"https://ijcai.org/proceedings/2023/547","pdf":"https://ijcai.org/proceedings/2023/0547.pdf","markdown":"https://ijcai.org/proceedings/2023/547.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/547","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":547,"pages":{"first":4921,"last":4929},"keywords":["Machine Learning: ML: Representation learning","Multidisciplinary Topics and Applications: MDA: Health and medicine"],"videos":[],"references":[{"position":1,"raw":"[Athalye et al., 2018] Anish Athalye, Nicholas Carlini, and David Wagner. 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