SlowGuess/NAACL_2021_OCR huggingface dataset:
The key module of our model in diagnosis is the multi-granularity hierarchical graph (MHG) document modeling method based on clauses and entities. In the experiments, we subsequently connect our multi-granularity hierarchical graph network module after BiGRU (Mullenbach et al., 2018) and MultiResCNN (Li and Yu, 2020) to further encode the EMRs. Since the clause categories are not labeled on this dataset, we only used the entity labels obtained by NER and do not constrain the clause node.
The result shown in Table 5 show that our module achieves effective performance improvements on all metrics based on MultiResCNN and Bi-GRU. With our module, BiGRU even surpasses
MultiResCNN in some metrics, while they originally have a huge gap in performance. This experimental result proves the effectiveness of our model in diagnosis on the benchmark data.
B Data and Metrics Description
B.1 Data Collection
We collected data from the real historical electronic medical records (EMRs) of the department of lymphedema. It contains the patient's self-complaint, history of present illness, past illness, personal history, family history, physical examination, and specialist examination. In order to protect the privacy of patients, we have deleted all content related to personal information. For the experiment, we extracted three types of EMRs of primary lymphedema, secondary lymphedema, and chyle reflux lymphedema from all EMRs. In addition, we added  $25\%$  of the confounded EMRs which includes patients who were hospitalized in the department of lymphedema, but the final diagnosis was other diseases. The statistics of four diseases in the final dataset are shown in Table 1.
Although the EMR distinguishes information such as the history of present illness and past illness, since the content of each part is still irregular text, and most of the existing EMRs are not standardized, we treat the EMR as an unstructured text and connect all the pieces together. Since our EMRs contain a complete physical examination and life history, most of the symptomatic entities present are negative and unrelated to diagnosis, which introduces a lot of noise into diagnosis and explanation. This is also an important reason that we cannot use entities as supporting facts. We do not have permission from hospitals to publish the
Type	Label
Clause	null(无标签), nature of symptom(症状的性质), relation of symptom(症状之间的联系), cause of change in symptom(引起症状变化的因素), position of symptom(症状的部位), duration of symptom(症状持续时间), time of onset of symptom(症状出现的时间), degree of symptom(症状的程度), new symptoms(新出现的症状), change of symptom(症状的变化), cause of disease(起病原因), time of disease(患病时间), severity of disease(疾病的严重程度), method of examination(检查方法), result of examination(检查结果), method of treatment(治疗方法), location of treatment(治疗地点), cause of treatment(治疗原因), purpose of treatment(治疗目的), effect of treatment(治疗效果), doses of drug(药物剂量), sleep condition(睡眠情况), mental condition(精神情况), defecation and urination condition(大小便情况), weight condition(体重情况), appetite condition(食欲情况), negative information(阴性资料), description of number(数量描述), name of drug(药物名称), physical description(体力描述), time description(时间描述), general description(通用描述), others(其它),
Entity	position(部位), drug(药品), duration(时长), disease(疾病), hospital(医院), surgery(手术), number(数字), examination(检查), time(时间), others(其他)
Table 6: The detailed description of the cluster label.
Chinese EMR data since they are legally protected by the laws. So we can only provide two examples in Table 7 with extraction results of each model.
B.2 Clustering Label
The detailed description of the cluster label is shown in Table 6. They are derived from the textbook "Diagnostics" (Xuehong Wan, 2013). It's a scientific classification method in medicine. In our experimental data, we manually annotated the corresponding labels on our own platform. These labels are crude, disease-independent and there may be intersections between categories, because they are only used to cluster clauses or entities and do not require a high degree of accuracy. Therefore, we can easily annotate a small part of the dataset manually and train a text classifier to classify the remaining data based on BERT (Devlin et al., 2019). The experimental results show that the classifier we trained on  $30\%$  of the data can achieve the annotation accuracy of  $80.76\%$  on clauses and  $97.13\%$  on entities in the remaining data.
B.3 Diagnostic Criterion
In this section, we will briefly introduce the diagnostic criteria for three types of lymphedema. Different hospital or even departments have their own ways to describe the recognized diagnostic criteria, which will be reflected in the EMRs. So our diagnostic criteria are manually annotated by analyzing the EMRs and the diagnosis guide (Levine, 2017). They are the re-descriptions of diagnostic criteria in the guide using phrases from EMRs.
Secondary Lymphedema For secondary lymphedema, the most important diagnostic criterion is whether the patient's lymphatic vessels have been damaged. Therefore, if there are descriptions related to tumors, surgery, radiotherapy, etc. in the medical records, it is likely to be secondary lymphedema.
Primary Lymphedema For primary lymphedema, the main basis for diagnosis is whether the patient's lymphatic vessels have congenital dysplasia or edema. Since there are few descriptions of this basis in the medical records, we will also take "edema without an inducement many years ago(多年前无诱因出现水肿)" as the basis for correct extraction in the evaluation.
Chylous Reflux Lymphedema For chylous reflux lymphedema, the key to the diagnosis is whether the patient has chylous reflux. Therefore, if there are descriptions related to milky white fluid, effusion reflux, etc. in the medical record, it is roughly considered to be chylous reflux lymphedema.
Table 7: The form of the EMR and the result of the extraction.