{"type":"ScholarlyArticle","title":"Automatic Emergency Diagnosis with Knowledge-Based Tree Decoding","authors":[{"name":"Ke Wang","slug":"ke-wang","given":"Ke","family":"Wang","affiliation":"Institute for Artificial Intelligence , Department of Computer Science and Technology , Tsinghua University , Beijing 100084 , China; Tsinghua-Fuzhou Institute of Digital Technology , Beijing National Research Center for Information Science and Technology , Tsinghua University , Beijing 100084 , China"},{"name":"Xuyan Chen","slug":"xuyan-chen","given":"Xuyan","family":"Chen","affiliation":"Beijing Tsinghua Changgung Hospital , School of Clinical Medicine , Tsinghua University , Beijing , China"},{"name":"Ning Chen","slug":"ning-chen","given":"Ning","family":"Chen","affiliation":"Institute for Artificial Intelligence , Department of Computer Science and Technology , Tsinghua University , Beijing 100084 , China; Tsinghua-Fuzhou Institute of Digital Technology , Beijing National Research Center for Information Science and Technology , Tsinghua University , Beijing 100084 , China"},{"name":"Ting Chen","slug":"ting-chen","given":"Ting","family":"Chen","affiliation":"Institute for Artificial Intelligence , Department of Computer Science and Technology , Tsinghua University , Beijing 100084 , China; Tsinghua-Fuzhou Institute of Digital Technology , Beijing National Research Center for Information Science and Technology , Tsinghua University , Beijing 100084 , China"}],"abstract":"Automatic diagnosis based on clinical notes is critical especially in the emergency department, where a fast and professional result is vital in assuring proper and timely treatment. Previous works formalize this task as plain text classification and fail to utilize the medically significant tree structure of International Classification of Diseases (ICD) coding system. Besides, external medical knowledge is rarely used before, and we explore it by extracting relevant materials from Wikipedia or Baidupedia. In this paper, we propose a knowledge-based tree decoding model (K-BTD), and the inference procedure is a top-down decoding process from the root node to leaf nodes. The stepwise inference procedure enables the model to give support for decision at each step, which visualizes the diagnosis procedure and adds to the interpretability of final predictions. Experiments on real-world data from the emergency department of a large-scale hospital indicate that the proposed model outperforms all baselines in both micro-F1 and macro-F1, and reduce the semantic distance dramatically.","doi":"10.24963/ijcai.2020/471","url":"https://ijcai.org/proceedings/2020/471","pdf":"https://ijcai.org/proceedings/2020/0471.pdf","markdown":"https://ijcai.org/proceedings/2020/471.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/471","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":471,"pages":{"first":3407,"last":3414},"keywords":["Multidisciplinary Topics and Applications: Biology and Medicine","Natural Language Processing: Text Classification","AI Ethics: Explainability","Natural Language Processing: NLP Applications and Tools"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24928/file_24928.m3u8","length_sec":291,"provider":"hls"}],"references":[{"position":1,"raw":"[Beygelzimer et al., 2009] Alina Beygelzimer, John Lang- ford, Yuri Lifshits, Gregory Sorkin, and Alex Strehl. 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