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Methods Inf Med
DOI: 10.1055/a-2576-1847
Original Article

ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data

Zixin Shu   *
1   Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, People's Republic of China
2   Institute of Liver Diseases, Hubei Key Laboratory of the Theory and Application Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, People's Republic of China
3   Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, People's Republic of China
4   Hubei Province Academy of Traditional Chinese Medicine, Wuhan, People's Republic of China
,
Rui Hua   *
1   Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, People's Republic of China
,
Dengying Yan
1   Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, People's Republic of China
,
Chenxia Lu
2   Institute of Liver Diseases, Hubei Key Laboratory of the Theory and Application Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, People's Republic of China
3   Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, People's Republic of China
4   Hubei Province Academy of Traditional Chinese Medicine, Wuhan, People's Republic of China
,
Meng Ren
2   Institute of Liver Diseases, Hubei Key Laboratory of the Theory and Application Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, People's Republic of China
3   Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, People's Republic of China
4   Hubei Province Academy of Traditional Chinese Medicine, Wuhan, People's Republic of China
,
Hong Gao
1   Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, People's Republic of China
,
Ning Xu
5   National Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Jun Li
6   Clinical College of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, People's Republic of China
,
Hui Zhu
2   Institute of Liver Diseases, Hubei Key Laboratory of the Theory and Application Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, People's Republic of China
3   Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, People's Republic of China
4   Hubei Province Academy of Traditional Chinese Medicine, Wuhan, People's Republic of China
,
Jia Zhang
2   Institute of Liver Diseases, Hubei Key Laboratory of the Theory and Application Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, People's Republic of China
3   Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, People's Republic of China
4   Hubei Province Academy of Traditional Chinese Medicine, Wuhan, People's Republic of China
,
Dan Zhao
3   Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, People's Republic of China
4   Hubei Province Academy of Traditional Chinese Medicine, Wuhan, People's Republic of China
,
Chenyang Hui
3   Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, People's Republic of China
4   Hubei Province Academy of Traditional Chinese Medicine, Wuhan, People's Republic of China
,
Chu Liao
3   Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, People's Republic of China
4   Hubei Province Academy of Traditional Chinese Medicine, Wuhan, People's Republic of China
,
Junqiu Ye
3   Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, People's Republic of China
4   Hubei Province Academy of Traditional Chinese Medicine, Wuhan, People's Republic of China
,
Qi Hao
3   Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, People's Republic of China
4   Hubei Province Academy of Traditional Chinese Medicine, Wuhan, People's Republic of China
,
Xinyan Wang
1   Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, People's Republic of China
,
Xiaodong Li
2   Institute of Liver Diseases, Hubei Key Laboratory of the Theory and Application Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, People's Republic of China
3   Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, People's Republic of China
4   Hubei Province Academy of Traditional Chinese Medicine, Wuhan, People's Republic of China
,
Baoyan Liu
5   National Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Xiaji Zhou
7   Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Runshun Zhang
7   Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, People's Republic of China
,
Min Xu
8   Information Technology Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People's Republic of China
,
Xuezhong Zhou
1   Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, People's Republic of China
› Author Affiliations Funding This work is partially supported by the National Natural Science Foundation of China (82174533 and 82204941), the Natural Science Foundation of Beijing (M21012), and the Key Project of Hubei Natural Science Foundation (2020CFA023).
› Further Information
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Abstract

Background

Symptom phenotypes are crucial for diagnosing and treating various disease conditions. However, the diversity of symptom terminologies poses a significant challenge to analyzing and sharing of symptom-related medical data, particularly in the field of traditional Chinese medicine (TCM). This study aims to construct an Integrated Symptom Phenotype Ontology (ISPO) to support data mining of Chinese electronic medical records (EMRs) and real-world studies in the TCM field.

Methods

We manually annotated and extracted symptom terms from 21 classical TCM textbooks and 78,696 inpatient EMRs, and integrated them with five publicly available symptom-related biomedical vocabularies. Through a human–machine collaborative approach for terminology editing and ontology development, including term screening, semantic mapping, and concept classification, we constructed a high-quality symptom ontology that integrates both TCM and Western medical terminology.

Results

ISPO provides 3,147 concepts, 23,475 terms, and 23,363 hierarchical relationships. Compared with international symptom-related ontologies such as the Symptom Ontology, ISPO offers significant improvements in the number of terms and synonymous relationships. Furthermore, evaluation across three independent curated clinical datasets demonstrated that ISPO achieved over 90% coverage of symptom terms, highlighting its strong clinical usability and completeness.

Conclusion

ISPO represents the first clinical ontology globally dedicated to the systematic representation of symptoms. It integrates symptom terminologies from historical and contemporary sources, encompassing both TCM and Western medicine, thereby enhancing semantic interoperability across heterogeneous medical data sources and clinical decision support systems in TCM.

Keywords

medical ontology - symptom phenotypes - traditional Chinese medicine - biomedical terminology - electronic medical records

Data Availability Statement

ISPO is publicly available as a free web resource (http://www.tcmkg.com/ISPO/home) and has been uploaded to BioPortal since May 2023.


Ethical Approval Statement

This study only utilizes clinical symptom terminology and does not involve human participants, patient data, or identifiable personal information.


Authors' Contribution

X.Z.Zhou, M.X., R.S.Zhang, X.J.Zhou, X.D.L., and B.Y. Liu conceived the study. Z.X.S. and R.H. analyzed the data. Z.X.S., R.H., and X.Z.Zhou drafted and revised the manuscript. All authors provided important contributions to data collection, processing, and review. All authors have proofread the manuscript.


* These authors contributed equally.


Supplementary Material



Publication History

Received: 04 February 2025

Accepted: 01 April 2025

Article published online:
06 May 2025

© 2025. Thieme. All rights reserved.

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

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