https://scholars.lib.ntu.edu.tw/handle/123456789/616947
標題: | Disease Classification on Admission and on Discharge with Residual CNN-Transformer | 作者: | Lin, Yu Ting Wei, Sheng Lun Huang, Hen Hsen HUI-CHIH WANG HSIN-HSI CHEN |
關鍵字: | Automatic Diagnosis | ICD Coding | Text Classification | 公開日期: | 14-十二月-2021 | 來源出版物: | ACM International Conference Proceeding Series | 摘要: | Clinical professionals perform disease classification on both admission and discharge of a patient, but previous works ignore the former. Physicians make a preliminary diagnosis based solely on current observations such as chief complaint and present illness at the admission time. Only limited information is available to decide which examination or treatment to make afterward. On discharge, complete medical records during hospitalization are available for deciding the International Classification of Diseases (ICD) code. Either occasion should be covered in a comprehensive disease classification system to meet the reality. Besides, from the technical perspective, previous state-of-the-art models employ the per-label attention mechanism to aggregate the contextualized vectors, less capable of handling the multi-label classification task up to 8,921 codes. In this paper, we conduct a comprehensive study on disease classification on both the admission and the discharge of patients. Furthermore, we propose a novel multi-head label decoding method that can replace the per-label attention module adopted by previous works. Experimental results show that our model achieves state-of-the-art performance in both admission and discharge scenarios. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/616947 | ISBN: | 9781450391153 | DOI: | 10.1145/3486622.3493946 |
顯示於: | 醫學院附設醫院 (臺大醫院) |
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