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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (10): 46-56    DOI: 10.11925/infotech.2096-3467.2022.0085
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Analyzing Structures of Medical Imaging Diagnosis Reports
Sheng Yu(),Hu Huirong,Wang Congcong,Yang Shengyi
School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Abstract  

[Objective] This paper tries to turn medical imaging diagnosis reports into structured data, aiming to effectively extract information from these free-text-reports. [Methods] First, we analyzed the text characteristics of medical imaging diagnosis reports, and proposed a structuring method based on entity recognition and rule extraction. Then, we annotated 800 reports to construct datasets for model evaluation. [Results] The proposed method had a precision rate of 0.87 for all entities from the medical imaging diagnostic reports, which was 4.03% higher than that of the BERT-BiLSTM-CRF. Its recall rate was also 2.81% higher than that of the BERT-BiLSTM-CRF. Compared with the method of dependency analysis, the proposed model improved the recognition precision of medical exam items and results by 5.62% and 2.31%. [Limitations] We only examined the proposed method with diagnostic PET-CT imaging reports from one hospital. [Conclusions] This study successfully converts the free texts of medical imaging diagnostic reports to structured data. It not only optimizes the classification, storage, and retrieval of medical reports, but also provides supports for future research on medical imaging.
Key words： Medical Imaging Diagnosis Report      Entity Recognition      Rule Extraction      Structure     
Received: 28 January 2022      Published: 16 November 2022
ZTFLH:  TP391  
Fund:National Natural Science Foundation of China(61877059)
Corresponding Authors: Sheng Yu,ORCID：0000-0002-6347-0769      E-mail: shengyu@csu.edu.cn
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Yu Sheng
Huirong Hu
Congcong Wang
Shengyi Yang

Cite this article:

Sheng Yu, Hu Huirong, Wang Congcong, Yang Shengyi. Analyzing Structures of Medical Imaging Diagnosis Reports. Data Analysis and Knowledge Discovery, 2022, 6(10): 46-56.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0085     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I10/46

影像描述 诊断结果
胸部PET-CT图像显示右胸廓内可见大片无肺纹理的透亮影及水样密度影，约占右侧胸腔70%。余下肺野显示大片软组织密度影，其内仅显示部分肺纹理影和气管影。该区域PET显示大片不均匀的糖代谢增高影，SUVmax为18.5，… 1. 右肺下叶基底段显示糖代谢异常增高，考虑恶性肿瘤，肺癌可能性大
2. 右肺门及纵隔多个淋巴结增大并代谢增高，考虑淋巴结转移
3. 右肺下叶感染和肺不张
…
Schematic of the PET-CT Imaging Diagnosis Report
实体名字 实体定义 实体实例
检查部位（BP） 影像检查的脏器名称 大脑、肺、甲状腺、肝脏…
属性（BPC） 脏器的属性 比例、密度、体积、大小…
谓词（PW） 说明脏器与征象之间关系 见、弥漫、呈、无、存在…
连接词（JW） 说明前后并列和递进等关系 及、同样、和、并、与…
征象（DI） 体征相关的描述 膨胀、钙化灶、糖代谢、楔形变…
间接征象（DIC） 对可见征象的进一步描述 短径、最大SUV、最大横截面…
Entity Definition for Medical Imaging Diagnosis Report
类别 数目
身体部位 31 425
属性 3 856
谓词 20 395
连接词 11 999
征象 30 893
间接征象 3 975
Annotated Medical Imaging Diagnosis Report Dataset
Flow of Structured Algorithm for Medical Imaging Diagnosis Report
分词工具 错误率
jieba 0.321 5
snownlp 0.548 6
pkuseg 0.387 5
thunlp 0.407 7
Performance of Different Tools in Medical Imaging Diagnosis Report
Structure of the Dic-BiLSTM-CRF Model
Structure of Embedding
Structure of BiLSTM
实体类型 P R F1
BP 0.948 5 0.934 9 0.941 6
BPC 0.883 8 0.900 7 0.892 2
DI 0.941 8 0.930 0 0.935 9
DIC 0.879 1 0.794 7 0.834 8
PW 0.956 2 0.951 6 0.953 8
JW 0.983 8 0.988 4 0.986 1
Recognition Result of Dic-BiLSTM-CRF
模型 P R F1
CNN-CRF 0.849 2 0.827 3 0.863 7
IDCNN-CRF 0.852 7 0.818 6 0.833 5
BiLSTM-CRF 0.857 2 0.851 3 0.853 8
BiLSTM-Attention-CRF 0.826 5 0.796 1 0.809 1
BERT-BiLSTM-CRF 0.852 2 0.851 1 0.850 9
本文模型 0.892 5 0.879 2 0.885 4
Recognition Result of Different Models in Medical Imaging Diagnosis Report
模型 无词典 有词典
P R F1 P R F1
CNN-CRF 0.849 2 0.827 3 0.836 7 0.855 2 0.855 7 0.855 1
IDCNN-CRF 0.852 7 0.818 6 0.833 5 0.842 6 0.852 4 0.847 4
BiLSTM-Attention-CRF 0.826 5 0.796 1 0.809 1 0.823 1 0.826 5 0.823 9
BiLSTM-CRF 0.857 2 0.851 3 0.853 8 0.892 5 0.879 2 0.885 4
Entity Recognition Comparison Result in Medical Imaging Diagnosis Report with or Without Dictionary
结构化
方法		 检查项 检查结果
P R F1 P R F1
人工规则 0.923 0 0.720 5 0.809 2 0.855 2 0.431 2 0.573 3
依存分析 0.887 5 0.834 3 0.860 0 0.887 2 0.786 1 0.833 5
ERRE 0.943 7 0.867 4 0.903 9 0.910 3 0.844 3 0.876 0
Comparison Results of the Structured Medical Imaging Diagnostic Reports
医学影像诊断报告原文 医学影像诊断报告结构化数据
CT平扫示脑实质密度均匀，未见异常密度灶;脑室系统扩大，形态如常，脑沟、脑裂增宽，脑中线结构居中。 脑实质 密度均匀
脑实质 未见异常密度灶
脑室系统 扩大
脑室系统 形态如常
脑沟 增宽
脑裂 增宽
脑中线 结构居中
Structured Data for Medical Imaging Diagnostic Reports
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