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

• URL: http://arxiv.org/abs/2407.12851v1
• Date: Mon, 8 Jul 2024 15:23:50 GMT
• Title: ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data
• Authors: Zixin Shu, Rui Hua, Dengying Yan, Chenxia Lu, Ning Xu, Jun Li, Hui Zhu, Jia Zhang, Dan Zhao, Chenyang Hui, Junqiu Ye, Chu Liao, Qi Hao, Wen Ye, Cheng Luo, Xinyan Wang, Chuang Cheng, Xiaodong Li, Baoyan Liu, Xiaji Zhou, Runshun Zhang, Min Xu, Xuezhong Zhou,
• Abstract summary: This study aimed to construct an Integrated Ontology of symptom phenotypes (ISPO) to support the data mining of Chinese EMRs and real-world study in TCM field.
• Score: 24.36545694430613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Symptom phenotypes are one of the key types of manifestations for diagnosis and treatment of various disease conditions. However, the diversity of symptom terminologies is one of the major obstacles hindering the analysis and knowledge sharing of various types of symptom-related medical data particularly in the fields of Traditional Chinese Medicine (TCM). Objective: This study aimed to construct an Integrated Ontology of symptom phenotypes (ISPO) to support the data mining of Chinese EMRs and real-world study in TCM field. Methods: To construct an integrated ontology of symptom phenotypes (ISPO), we manually annotated classical TCM textbooks and large-scale Chinese electronic medical records (EMRs) to collect symptom terms with support from a medical text annotation system. Furthermore, to facilitate the semantic interoperability between different terminologies, we incorporated public available biomedical vocabularies by manual mapping between Chinese terms and English terms with cross-references to source vocabularies. In addition, we evaluated the ISPO using independent clinical EMRs to provide a high-usable medical ontology for clinical data analysis. Results: By integrating 78,696 inpatient cases of EMRs, 5 biomedical vocabularies, 21 TCM books and dictionaries, ISPO provides 3,147 concepts, 23,475 terms, and 55,552 definition or contextual texts. Adhering to the taxonomical structure of the related anatomical systems of symptom phenotypes, ISPO provides 12 top-level categories and 79 middle-level sub-categories. The validation of data analysis showed the ISPO has a coverage rate of 95.35%, 98.53% and 92.66% for symptom terms with occurrence rates of 0.5% in additional three independent curated clinical datasets, which can demonstrate the significant value of ISPO in mapping clinical terms to ontologies.

Related papers

• Efficient Biomedical Entity Linking: Clinical Text Standardization with Low-Resource Techniques [0.0]
  Multiple terms can refer to the same core concepts which can be referred as a clinical entity. Ontologies like the Unified Medical Language System (UMLS) are developed and maintained to store millions of clinical entities. We propose a suite of context-based and context-less remention techniques for performing the entity disambiguation.
  arXiv  Detail & Related papers  (2024-05-24T01:14:33Z)
• Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric [1.7802147489386628]
  We introduce a novel approach designed specifically for assessing the semantic similarity between generated medical reports and the ground truth. Our approach is validated, demonstrating its efficiency in assessing domain-specific semantic similarity within medical contexts.
  arXiv  Detail & Related papers  (2024-02-19T07:48:25Z)
• PMC-LLaMA: Towards Building Open-source Language Models for Medicine [62.39105735933138]
  Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding. LLMs struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. We describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA.
  arXiv  Detail & Related papers  (2023-04-27T18:29:05Z)
• sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansion [0.0]
  sEHR-CE is a novel framework based on transformers to enable integrated phenotyping and analyses of heterogeneous clinical datasets. We validate our approach using primary and secondary care data from the UK Biobank, a large-scale research study.
  arXiv  Detail & Related papers  (2022-11-30T16:00:43Z)
• Cross-Lingual Knowledge Transfer for Clinical Phenotyping [55.92262310716537]
  We investigate cross-lingual knowledge transfer strategies to execute this task for clinics that do not use the English language. We evaluate these strategies for a Greek and a Spanish clinic leveraging clinical notes from different clinical domains. Our results show that using multilingual data overall improves clinical phenotyping models and can compensate for data sparseness.
  arXiv  Detail & Related papers  (2022-08-03T08:33:21Z)
• EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
  This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
  arXiv  Detail & Related papers  (2022-08-01T18:47:03Z)
• Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes [13.096008602034086]
  Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. We propose a method using brain and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT) The weakly supervised approach is proposed to learn a confirmation phenotype model to improve Text-to-UMLS linking, without annotated data from domain experts.
  arXiv  Detail & Related papers  (2022-05-11T17:38:24Z)
• Self-supervised Answer Retrieval on Clinical Notes [68.87777592015402]
  We introduce CAPR, a rule-based self-supervision objective for training Transformer language models for domain-specific passage matching. We apply our objective in four Transformer-based architectures: Contextual Document Vectors, Bi-, Poly- and Cross-encoders. We report that CAPR outperforms strong baselines in the retrieval of domain-specific passages and effectively generalizes across rule-based and human-labeled passages.
  arXiv  Detail & Related papers  (2021-08-02T10:42:52Z)
• Clinical Named Entity Recognition using Contextualized Token Representations [49.036805795072645]
  This paper introduces the technique of contextualized word embedding to better capture the semantic meaning of each word based on its context. We pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) Explicit experiments show that our models gain dramatic improvements compared to both static word embeddings and domain-generic language models.
  arXiv  Detail & Related papers  (2021-06-23T18:12:58Z)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)
• Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Ontology and Corpus-based Semantic Representation for Biomedical Concepts [0.5812284760539712]
  This paper introduces Multi-Ontology Embeddings (MORE), a framework for incorporating domain knowledge from multiple ontologies into a distributional semantic model. We use the RadCore and MIMIC-III free-text datasets for the corpus-based component of MORE. For the corpus-based part, we use the Medical Subject Headings (MeSH) and three state-of-the-art-based similarity measures.
  arXiv  Detail & Related papers  (2020-04-14T14:38:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.