{"type":"ScholarlyArticle","title":"The Graph-based Mutual Attentive Network for Automatic Diagnosis","authors":[{"name":"Quan Yuan","slug":"quan-yuan","given":"Quan","family":"Yuan","affiliation":"Baidu Inc , Beijing , China"},{"name":"Jun Chen","slug":"jun-chen","given":"Jun","family":"Chen","affiliation":"Baidu Inc , Beijing , China"},{"name":"Chao Lu","slug":"chao-lu","given":"Chao","family":"Lu","affiliation":"Baidu Inc , Beijing , China"},{"name":"Haifeng Huang","slug":"haifeng-huang","given":"Haifeng","family":"Huang","affiliation":"Baidu Inc , Beijing , China"}],"abstract":"The automatic diagnosis has been suffering from the problem of inadequate reliable corpus to train a trustworthy predictive model. Besides, most of the previous deep learning based diagnosis models adopt the sequence learning techniques (CNN or RNN), which is difficult to extract the complex structural information, e.g. graph structure, between the critical medical entities. In this paper, we propose to build the diagnosis model based on the high-standard EMR documents from real hospitals to improve the accuracy and the credibility of the resulting model. Meanwhile, we introduce the Graph Convolutional Network into the model that alleviates the sparse feature problem and facilitates the extraction of structural information for diagnosis. Moreover, we propose the mutual attentive network to enhance the representation of inputs towards the better model performance. The evaluation conducted on the real EMR documents demonstrates that the proposed model is more accurate compared to the previous sequence learning based diagnosis models. The proposed model has been integrated into the information systems in over hundreds of primary health care facilities in China to assist physicians in the diagnostic process.","doi":"10.24963/ijcai.2020/469","url":"https://ijcai.org/proceedings/2020/469","pdf":"https://ijcai.org/proceedings/2020/0469.pdf","markdown":"https://ijcai.org/proceedings/2020/469.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/469","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":469,"pages":{"first":3393,"last":3399},"keywords":["Machine Learning Applications: Bio/Medicine","Multidisciplinary Topics and Applications: Biology and Medicine","Natural Language Processing: NLP Applications and Tools"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24711/file_24711.m3u8","length_sec":284,"provider":"hls"}],"references":[{"position":1,"raw":"[Anandan et al., 2019] Padmanabhan Anandan, Yan Huang, Kazumi Nishikawa, BBorie Park, Eric S. 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