MedWriter: Knowledge-Aware Medical Text Generation

Proceedings of the 28th International Conference on Computational Linguistics, pages 2363–2368 Barcelona, Spain (Online), December 8-13, 2020 2363 MedWriter: Knowledge-Aware Medical Text Generation Youcheng Pan1†, Qingcai Chen1,2, Weihua Peng3, Xiaolong Wang1, Baotian Hu1∗, Xin Liu1, Junying Chen1, Wenxiu Zhou1 1Harbin Institute of Technology, Shenzhen 2Peng Cheng Laboratory 3Baidu International Technology (Shenzhen) Co., Ltd panyoucheng4@gmail.com {qingcai.chen, xlwangsz, hubaotian}@hit.edu.cn pengweihua@baidu.com Abstract To exploit the domain knowledge to guarantee the correctness of generated text has been a hot topic in recent years, especially for high professional domains such as medical. However, most of recent works only consider the information of unstructured text rather than structured information of the knowledge graph. In this paper, we focus on the medical topic-to-text generation task and adapt a knowledge-aware text generation model to the medical domain, named MedWriter, which not only introduces the specific knowledge from the external MKG but also is capable of learning graph-level representation. We conduct experiments on a medical literature dataset collected from medical journals, each of which has a set of topic words, an abstract of medical literature and a corresponding knowledge graph from CMeKG. Experimental results demonstrate incorporating knowledge graph into generation model can improve the quality of the generated text and has robust superiority over the competitor methods. 1 Introduction Medical text generation has been a hot topic recently, such as electronic medical record (EMR) gen- eration (Guan et al., 2018), medical question generation (Zhang et al., 2018), clinical notes genera- tion (Melamud and Shivade, 2019), etc. However, compared to the research in the general domain, there is still a lot of space for exploration, especially with the assistance of specific knowledge graph. celecoxib treat ADRs KOA pain topic 塞来昔布，骨创伤治疗仪，膝骨关节炎，疼痛 celecoxib, bone trauma instrument, knee osteoarthritis (KOA), pain knowledge graph <塞来昔布，治疗，膝骨关节炎> <celecoxib, treat, KOA> <膝骨关节炎，临床症状，疼痛> <KOA, clinical symptom, pain> <塞来昔布，不良反应，疼痛> <celecoxib, adverse drug reactions (ADRs), pain> text 目的： 探讨塞来昔布联合骨创伤治疗仪治疗膝关节炎疼痛的疗效。方法：将聊城市第三人民医院骨科门诊 2015 年 1 月—2017 年 1 月收治的 108 例单侧早中期膝骨性关节炎患者随机分 为观察组、对照组，每组 54 例。观察组口服塞来昔布加上应用骨创伤治疗仪；对照组口服塞来昔布。比较两组患者临床疗效。结果：末次随访时，观察组患者疼痛评分均低于对照组 （t=3.21，p=0.00），观察组患者膝关节功能优于对照组（t=3.74，p=0.00），观察组总有效率 88.89%高于对照组的 77.78%（χ2=4.70，p=0.03）。结论：塞来昔布联合骨创伤治疗仪临床 疗效明显，有效降低疼痛评分，能增进膝关节功能改善，值得临床应用。 Objective: Explore the therapeutic effect of celecoxib combined with bone trauma treatment instrument on knee arthritis pain. Methods: 108 patients with unilateral early and mid-stage knee osteoarthritis treated in the orthopedic clinic of liaocheng third people's hospital from January 2015 to January 2017 were randomly divided into observation group and contrast group, with 54 cases in each group. The observation group takes celecoxib orally with the application of a bone trauma treatment device; The contrast group just takes celecoxib orally. The clinical effects of the two groups were compared. Results: At the last follow-up, the pain score of the observation group was lower than that of the contrast group (t=3.21,p=0.00). The knee function of the observation group was better than that of the contrast group (t=3.74,p=0.00). The total effective rate in the observation group was 88.89% higher than 77.78% in the contrast group (χ2=4.70,p=0.03). Conclusion: Celecoxib combined with bone trauma treatment device has obvious clinical effect, effectively reduces pain score, can improve knee function, and is worthy of clinical application. Figure 1: An example of …

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