AI-powered TCM Diagnosis: A Multi-Task Learning Approach for Syndrome Element Diagnosis and Syndrome Differentiation

AI-powered TCM Diagnosis: A Multi-Task Learning Approach for Syndrome Element Diagnosis and Syndrome Differentiation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI-powered TCM Diagnosis: A Multi-Task Learning Approach for Syndrome Element Diagnosis and Syndrome Differentiation Yan Lyu, Xiangnan Feng, Ping Jiang, Haoxuan Li, Xinxing Lai, Xiao-Hua Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9200456/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Syndrome differentiation is the cornerstone of Traditional Chinese Medicine (TCM). The development of robust artificial intelligence aided diagnosis (AIAD) syndrome differentiation method is therefore a pivotal direction for TCM modernization. Contemporary TCM theory identifies syndrome elements as fundamental components essential for accurate syndrome differentiation. However, existing computational methods have not fully exploited their potential through joint learning approaches. This study proposes a novel AIAD framework that simultaneously learns syndrome element diagnosis and syndrome differentiation. The framework aims to enhance diagnostic accuracy and provide empirical validation of the theoretical role of syndrome elements within the TCM diagnostic process. Methods We developed a novel AIAD framework trained and tested on 6,226 electronic health records (EHRs) and further evaluated on an independent dataset of 1,057 EHRs derived from clinical guidelines. A multi-task learning approach was utilized to simultaneously model syndrome element diagnosis and syndrome differentiation. We proposed and compared two models: AIAD-A1, which considers syndrome elements as intermediate variables, and AIAD-A2, which treats them as mediators. Model performance was assessed using top-K accuracy and NDCG@K metrics. Results . The AIAD-A2 model, which treats syndrome elements as mediators, significantly outperformed both the AIAD-A1 model and baseline models across multiple evaluation metrics. AIAD-A2 improved top-K accuracy by up to 6.31% and demonstrated remarkable enhancements in handling long-tail data, improving top-15 accuracy by 21.34% within the tail subgroup. Additionally, it exhibited superior generalizability, with the smallest performance decline (7.77% in top-15 accuracy) on an independent test dataset facing a different data distribution, compared to drops of 22.7% for the baseline and 12.44% for AIAD-A1. Moreover, model visualization confirmed AIAD-A2’s capability to focus on the most clinically relevant symptoms. Conclusions Our findings reveal the role of syndrome elements as mediators in TCM syndrome differentiation. Accurate diagnosis necessitates the integrated consideration of both syndrome elements and the original symptom descriptions. The proposed AIAD-A2 framework offers an effective and generalizable approach for AI-powered TCM diagnosis, successfully addressing challenges such as data imbalance and distribution shifts. This work contributes to the modernization of TCM by delivering a robust AI method and deepening the theoretical understanding of the diagnostic process. Syndrome Differentiation Syndrome Element Multi-Task Learning Artificial Intelligence TCM Diagnosis Full Text Additional Declarations Competing interest reported. Xinxing Lai serves as a member of the Youth Editorial Board of Chinese Medicine. The author was not involved in the review or decision-making process for this manuscript. The authors declare no other conflicts of interest. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9200456","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611774005,"identity":"7c22764a-4592-4d56-a19b-670e7d2e6e9a","order_by":0,"name":"Yan Lyu","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Lyu","suffix":""},{"id":611774006,"identity":"6acb1ff0-20d9-4302-9e3d-7de85da7532c","order_by":1,"name":"Xiangnan Feng","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xiangnan","middleName":"","lastName":"Feng","suffix":""},{"id":611774007,"identity":"c6f6b421-60f4-4f9e-a5fa-e079a7e68014","order_by":2,"name":"Ping Jiang","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Jiang","suffix":""},{"id":611774008,"identity":"3422bba7-85f6-4244-95a7-ac8e7c7bce93","order_by":3,"name":"Haoxuan Li","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Haoxuan","middleName":"","lastName":"Li","suffix":""},{"id":611774009,"identity":"d8fbcfdf-278c-49dc-b22b-1ccfa323871d","order_by":4,"name":"Xinxing Lai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYDADfmbmww9IUJ/AwCDZzpZmQJoWg/M8ChJEKTZn7zFg/PnDLt/4MA+DAUONTTRBLZY9ZwyYeRKSLbcd5j3wgOFYWm4DIS0GN3K3/2ZIYDYwO8yXYMDYcJgILfffbmD8kVBvYNzMYyBBnJYbvBsYeBIOGwCdR6yWM/kfmHnSjhtIHAYGcgJRfjl+LIHxh021AX//4cMPPtTYENaCChJIUz4KRsEoGAWjABcAAMtXOduITmsaAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Xinxing","middleName":"","lastName":"Lai","suffix":""},{"id":611774010,"identity":"ba6720a1-b131-404d-9695-8995bceda8e7","order_by":5,"name":"Xiao-Hua Zhou","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Hua","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-03-23 12:24:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9200456/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9200456/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105573513,"identity":"ebad621d-fb05-4024-b7a7-496b08825c11","added_by":"auto","created_at":"2026-03-27 13:31:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1195613,"visible":true,"origin":"","legend":"","description":"","filename":"AIADCM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9200456/v1_covered_c6395c12-d811-4d21-9d2c-bdc2793ea952.pdf"}],"financialInterests":"Competing interest reported. 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The development of robust artificial intelligence aided diagnosis (AIAD) syndrome differentiation method is therefore a pivotal direction for TCM modernization. Contemporary TCM theory identifies syndrome elements as fundamental components essential for accurate syndrome differentiation. However, existing computational methods have not fully exploited their potential through joint learning approaches. This study proposes a novel AIAD framework that simultaneously learns syndrome element diagnosis and syndrome differentiation. The framework aims to enhance diagnostic accuracy and provide empirical validation of the theoretical role of syndrome elements within the TCM diagnostic process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed a novel AIAD framework trained and tested on 6,226 electronic health records (EHRs) and further evaluated on an independent dataset of 1,057 EHRs derived from clinical guidelines. A multi-task learning approach was utilized to simultaneously model syndrome element diagnosis and syndrome differentiation. We proposed and compared two models: AIAD-A1, which considers syndrome elements as intermediate variables, and AIAD-A2, which treats them as mediators. Model performance was assessed using top-K accuracy and NDCG@K metrics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e. The AIAD-A2 model, which treats syndrome elements as mediators, significantly outperformed both the AIAD-A1 model and baseline models across multiple evaluation metrics. AIAD-A2 improved top-K accuracy by up to 6.31% and demonstrated remarkable enhancements in handling long-tail data, improving top-15 accuracy by 21.34% within the tail subgroup. Additionally, it exhibited superior generalizability, with the smallest performance decline (7.77% in top-15 accuracy) on an independent test dataset facing a different data distribution, compared to drops of 22.7% for the baseline and 12.44% for AIAD-A1. Moreover, model visualization confirmed AIAD-A2’s capability to focus on the most clinically relevant symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings reveal the role of syndrome elements as mediators in TCM syndrome differentiation. Accurate diagnosis necessitates the integrated consideration of both syndrome elements and the original symptom descriptions. The proposed AIAD-A2 framework offers an effective and generalizable approach for AI-powered TCM diagnosis, successfully addressing challenges such as data imbalance and distribution shifts. This work contributes to the modernization of TCM by delivering a robust AI method and deepening the theoretical understanding of the diagnostic process.\u003c/p\u003e","manuscriptTitle":"AI-powered TCM Diagnosis: A Multi-Task Learning Approach for Syndrome Element Diagnosis and Syndrome Differentiation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 08:17:33","doi":"10.21203/rs.3.rs-9200456/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"149f8e58-faaf-44ed-a63e-9497b90fee5a","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T12:56:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 08:17:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9200456","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9200456","identity":"rs-9200456","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}