Previously submitted to: JMIR Medical Informatics (no longer under consideration since Oct 30, 2023)
Date Submitted: Oct 4, 2023
Open Peer Review Period: Oct 4, 2023 - Oct 30, 2023
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Incorporating Ontological Knowledge into the Multi-Label Classification of Traditional Chinese Medicine Symptom Entities Research
ABSTRACT
Background:
In the realm of AI-assisted traditional Chinese medicine (TCM) syndrome differentiation and disease diagnosis, precise symptom recognition and classification pose significant challenges. This is because TCM heavily relies on a nuanced understanding of symptoms to guide treatment decisions. However, current entity recognition models grapple with a range of issues, including limitations stemming from label space, resource-intensive computations, a lack of domain expertise to cover diverse symptom descriptions, and an over-reliance on large annotated datasets. Furthermore, the world of TCM symptom labels is vast and complex, with intricate correlations and the added complexity of label imbalance, all contributing to the complexity of the task.
Objective:
The goal of this study is to tackle the challenges associated with multi-class symptom entity recognition within the realm of TCM symptoms. To achieve this, we propose a two-stage entity classification approach and leverage ontology knowledge to enhance entity classification. Our approach aims to reduce the model's dependency on annotated data and address issues such as label imbalance and the expansive label space.
Methods:
We introduce an innovative multi-label entity classification model designed for accurate TCM symptom recognition. We establish a comprehensive TCM symptom ontology framework to standardize symptom descriptions. We use the BERT+BiLSTM+CRF method to identify multiple symptom entities in TCM medical records. In order to gain a deeper understanding of the relationships between multiple symptom entities within the text and the connections between different category labels, we introduce a multi-level correlation feature fusion module. Finally, we adopt a multi-label classification method based on a hierarchical label tree, effectively mitigating the challenges associated with label imbalance within TCM text.
Results:
Using authentic Qihuang TCM electronic medical records, our model significantly enhances efficiency and accuracy in multi-label symptom classification, achieving a Hamming Loss of 2.932 * 10^-2 and a Micro-F1 score of 0.8452.
Conclusions:
Our study delivers several notable contributions. Firstly, we construct a comprehensive TCM symptom ontology framework, successfully incorporating TCM domain knowledge into the model to bolster its foundational features. Secondly, we employ a multi-label classification approach for entity recognition, capturing the multiple labels and intricate relationships of symptom entities in TCM texts with heightened accuracy. Simultaneously, we introduce a hierarchical label tree to effectively mitigate the impact of symptom label imbalance on the model. Lastly, we introduce a multi-level correlation feature fusion module that comprehensively captures textual information, thereby improving model performance, enhancing label clustering, and ultimately elevating the overall quality and efficiency of the model. These contributions provide an effective methodology for TCM symptom extraction and are poised to make a significant impact on TCM research and practice.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.