Konwledge-Enabled Diagnosis Assistant Based on Obstetric EMR s and Knowledge Graph (2020)

Abstract: The obstetric Electronic Medical Record (EMR) contains a large amount of medical data and health information. It plays a vital role in improving the quality of the diagnosis assistant service. In this paper, we treat the diagnosis assistant as a multi-label classification task and propose a Knowledge-Enabled Diagnosis Assistant (KEDA) model for the obstetric diagnosis assistant. We utilize the numerical information in EMRs and the external knowledge from Chinese Obstetric Knowledge Graph (COKG) to enhance the text representation of EMRs. Specifically, the bidirectional maximum matching method and similarity-based approach are used to obtain the entities set contained in EMRs and linked to the COKG. The final knowledge representation is obtained by a weight-based disease prediction algorithm, and it is fused with the text representation through a linear weighting method. Experiment results show that our approach can bring about +3.53 F1 score improvements upon the strong BERT baseline in the diagnosis assistant task.

## introduction health service relations on the health of millions of people, and it is a livelihood issue in our country. specifically in china, which has a huge population, the total amount of medical resources is still insufficient. the imbalance between the supply and demand for medical services is still the focus of china's healthcare industry. although the implementation of china's universal two-child policy in 2016 achieved many benefits, it also leads to an increase in the proportion of older pregnant women and the incidence of various complications @xcite . compared to the overall supply of the medical industry, the lack of obstetric medical resources is prominent. since the issue of the basic norms of electronic medical records (trial) (china's ministry of health, 2010) by the national health and family planning medical affairs commission in 2010, medical institutions have accumulated many obstetric electronic medical records (emrs). emrs are detailed records of medical activities, dominated by the semi-structured or unstructured texts. there is a lot of medical knowledge and health information in emrs, which is the core medical big data. the first course record in emrs can be divided into the chief complaint, physical examination, auxiliary examination, admitting diagnosis, diagnostic basis, and treatment plan. in general, there is not a single diagnosis in the admitting diagnosis, it usually includes normal obstetric diagnosis, medical diagnosis, and complications. as a consequence, the diagnosis assistant task based on the chinese obstetric emrs can be treated as a multilabel text classification problem, in which the different diagnoses can be regarded as the variable labels. however, the doctor's diagnosis and treatment process are based on comprehensive clinical experience and knowledge in the medical field to make a diagnosis and formulate a corresponding treatment plan. at the same time, they can also explain the corresponding diagnosis basis to the patient in detail. therefore, rich clinical experience and solid medical knowledge play a vital role in the diagnosis procedure. in order to simulate the diagnosis and treatment process of doctors, we need to introduce external knowledge that is not available in emrs. the introduction of medical domain knowledge requires formal expression so that it can be easily used in the diagnosis assistant model. to solve this problem, we adopt the chinese obstetric knowledge graph (cokg) 0 to introduce external medical domain knowledge. in this paper, we use the bert (bidirectional encoder representation from transformers) @xcite to generate the text representation of emrs. the numerical information in emrs is also important for the diagnosis results, it is being used to enhance the text representation with the multi-head self-attention @xcite . for entity acquisition, we compare the bidirectional maximum matching method and the bi-lstm-crf method respectively, and choose the former method to obtain the entity sets from emrs. then the entities are linked to the cokg by a similarity-based method. due to the fact that the negative words in emrs will have an impact on the semantics, we employ a negative factor to deal with the negative words in emrs and propose a weight-based disease prediction algorithm to obtain the final knowledge representation. finally, a linear weighting method is employed to fuse the text representation and knowledge representation. the experiments on the obstetric first course record dataset support the effectiveness of our approach. the main contributions of this paper are summarized as follows: ## related work in this paper, we treat the obstetric diagnosis assistant task as a multi-label classification problem. the multi-label classification in traditional machine learning is usually regarded as a binary classification problem or adjust the existing algorithm to adapt to the multi-label classification task @xcite @xcite . with the development and application of deep learning, cnn and rnn are widely used in multi-label text classification tasks. for example, @xcite use cnn-based word embedding to obtain the direct relationship of the labels. @xcite propose a model that combined cnns and rnns to represent the semantic information of the text, and modeling the high-order label association. baker s and korhonen a (2017) use row mapping to hide the layers that map to the label co-occurrence based on a cnn architecture to improve the model performance. @xcite propose a multi-label classification algorithm based on cyclic neural networks for machine translation. @xcite propose a sequence generation model (sgm) to solve the multi-label classification problem. in recent years, the pre-training technology has grown rapidly, elmo @xcite , openai gpt @xcite , and bert @xcite model have achieved significant improvements in multiple natural language processing tasks. they can be applied to various tasks after fine-tuning. however, due to the little knowledge connection between specific and open domain, these models do not perform well on domain-specific tasks. one way to solve this problem is to pre-train the model on a specific domain, but it is time-consuming and computationally expensive for most users. the models in this way are like ernie @xcite , bert-wwm @xcite , span-bert @xcite , roberta @xcite , xlnet @xcite , and so on. moreover, if we can integrate knowledge at the fine-tuning process, it may bring better results. several studies integrate external knowledge into the model. @xcite (rakel), multi-label k-nearest neighbor (mlknn), and classifier chain (cc) to build the diagnosis assistant models. ma et al. (2018a) fuse numerical features by employing the concatenated vector to improve the performance of the diagnosis assistant. zhang et al. (2019) encode emrs with bert, and propose an enhanced layer to enhance the text representation for diagnosis assistant. ## conclusion in this paper, the obstetric diagnosis assistant task is treated as a multi-label classification problem. we propose a keda model for this task, which integrates the numerical information from emrs and external knowledge from cokg to improve the performance of diagnosis. we utilize the bidirectional maximum matching method to get the entities in emrs, and the similarity-based approach is used to link the entities in knowledge graph cokg. then we propose a wbdp algorithm to compute the weights of the entities in the candidate set. finally, a linear weighting method is employed to fuse the text representation and knowledge representation. the results on the obstetric emrs support the effectiveness of our approach compared to the bert model. it turns out that even though the pre-training of bert in the future, we will incorporate more valuable information into deep neural networks to further improve the performance of the diagnosis assistant. we find that some disease entities in emrs are not included in cokg(for example, the disease entity 'patella fracture' is a diagnosis label in emrs, but it is not an obstetric disease), to introduce other knowledge graphs that contain more disease entities is an effective feature for diagnosis.