MIE: A Medical Information Extractor towards Medical Dialogues

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6460–6469 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 6460 MIE: A Medical Information Extractor towards Medical Dialogues Yuanzhe Zhang1, Zhongtao Jiang1,2, Tao Zhang1,2, Shiwan Liu1,2, Jiarun Cao3⇤, Kang Liu1,2, Shengping Liu4 and Jun Zhao1,2 1 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China 2 School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China 3 National Centre for Text Mining, University of Manchester, Manchester, M1 7DN, United Kingdom 4 Beijing Unisound Information Technology Co., Ltd, Beijing, 100028, China {yzzhang, zhongtao.jiang, tao.zhang, shiwan.liu, kliu, jzhao}@nlpr.ia.ac.cn jiarun.cao@manchester.ac.uk, liushengping@unisound.com Abstract Electronic Medical Records (EMRs) have be- come key components of modern medical care systems. Despite the merits of EMRs, many doctors suffer from writing them, which is time-consuming and tedious. We believe that automatically converting medical dialogues to EMRs can greatly reduce the burdens of doc- tors, and extracting information from medical dialogues is an essential step. To this end, we annotate online medical consultation di- alogues in a window-sliding style, which is much easier than the sequential labeling an- notation. We then propose a Medical Infor- mation Extractor (MIE) towards medical di- alogues. MIE is able to extract mentioned symptoms, surgeries, tests, other information and their corresponding status. To tackle the particular challenges of the task, MIE uses a deep matching architecture, taking dialogue turn-interaction into account. The experimen- tal results demonstrate MIE is a promising solution to extract medical information from doctor-patient dialogues. 1 1 Introduction With the advancement of the informatization pro- cess of the medical system, Electronic Medical Records (EMRs) are required by an increasing number of hospitals all around the world. Com- pared with conventional medical records, EMRs are easy to save and retrieve, which bring consid- erable convenience for both patients and doctors. Furthermore, EMRs allow medical researchers to investigate the implicit contents included, such as epidemiologic study and patient cohorts finding. ⇤Contribution during internship at Institute of Automation, Chinese Academy of Sciences. 1Data and codes are available at https://github. com/nlpir2020/MIE-ACL-2020. Despite the advantages, most doctors complain that writing EMRs makes them exhausted (Wachter and Goldsmith, 2018). According to the study of Sin- sky et al. (2016), physicians spend nearly two hours doing administrative work for every hour of face- time with patients, and the most time-consuming aspect is inputting EMRs. We believe that automatically converting doctor- patient dialogues into EMRs can effectively remove the heavy burdens of doctors, making them more deliberate to communicate with their patients. One straightforward approach is the end-to-end learning, where more supervised data, i.e., dialogue-EMR pairs are needed. Unfortunately, such data is hard to acquire in medical domain due to the privacy policy. In this paper, We focus on extracting medi- cal information from dialogues, which we think is an essential step for EMR generation. Extracting information from medical dialogues is an emerging research field, and there are only few previous attempts. Finley et al. …

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