M ed W riter: Knowledge-Aware Medical Text Generation (2020)

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.

## introduction medical text generation has been a hot topic recently, such as electronic medical record (emr) generation @xcite , medical question generation @xcite , clinical notes generation @xcite , 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. 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 (@xmath0.21,@xmath1.00). the knee function of the observation group was better than that of the contrast group (@xmath2.74,@xmath3.00). the total effective rate in the observation group was 88.89% higher than 77.78% in the contrast group (χ2=4.70,@xmath4.03). 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. intuitively, the medical knowledge graph (kg) is essential to guarantee the correctness of generated text, especially for high professional domains. however, most of the recent works don't make full use of medical knowledge graph (mkg). @xcite adopt an encoder-decoder model to generate free texts in electronic health records and @xcite propose a gan-based framework trained by the reinforce algorithm to generate synthetic emr text, both of which don't utilize the external medical knowledge. @xcite incorporate medical concept embedding into the sequence-to-sequence gru gru gru gru ... ... 𝑤 " 𝑤 # 𝑤 $ 𝑤 % attention 𝐶 ' ( graph attention feed forward 𝑣 " 𝑣 # ...... 𝑣 * 𝑣 +," attention 𝐶 ' ℎ ' 𝑦′ ',# gru 𝑦′ '," gru 𝑦′ ' gru gen & copy 𝑦′ ' topic encoder graph encoder decoder ... ... model, which is pretrained by leveraging the medical concept unique identifiers from the umls, to improve the quality of generated clinical text. however, they view each triplet as a instance and adopt the skip-gram algorithm @xcite to acquire the pretrained concept embedding, which ignore the relationships between medical entities. in this paper, we focus on the knowledge-aware medical text generation, which is a topic-to-text task. we firstly collect a medical literature dataset from medical journals that contains more than 50,000 topic-text pairs. each of them has a set of keywords describing the topic and a relevant abstract as target text. for each pair, we collect the corresponding knowledge from a large scale chinese medical knowledge graph cmekg foot_0 . an example is shown in figure 1 . then, we adapt a knowledge-aware neural generation model for this task, named medwriter, which consists of three components: topic encoder, graph encoder and decoder. the topic encoder is used to acquire the representation of topic words, while the graph encoder exploits the specific information from the mkg. therefore, the model combines the information of topic words with the medical knowledge. afterwards, we use the decoder with copy mechanism @xcite to generate medical text. 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. ## method given a set of topic words @xmath5, w 2 , ..., w s }, and a knowledge graph represented as a set of triples, i.e., @xmath6, g 2 , g 3 , ...}, where each triple g i is comprised @xmath7, p i , o @xmath8, predicate and object respectively, our goal is to generate a natural language text @xmath9, y 2 , y 3 , ...}, which is required to be relevant to the topic, grammatically correct and informative. ## conclusion we use the medical knowledge graph to facilitate the medical text generation. a chinese medical literature dataset with the corresponding knowledge graph is collected and an encoder-decoder model equipped with a graph encoder is adapted to the medical topic-to-text generation task. experimental results demonstrate the effectiveness of incorporating knowledge graph into generation model by outperforming the competitor methods. this work is a preliminary attempt on knowledge-aware medical text generation. in the future, we plan to do more researches on applying natural language generation technology to the medical domain.