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Traditional approaches struggle to manage heterogeneous sources and to cope with the complex content structure of cases, which limits real-world performance. The MCoder-T model addresses this by integrating causal-to-mask attention, unified multimodal fusion, and multitask learning to jointly leverage text, medical images, and structured data. Experiments show MCoder-T outperforms baseline and progressive methods, improving results by 7% to 18% across evaluation indicators, while supporting reliable multimodal fusion and drop-coding automation.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/application-of-multimodal-data-fusion-and-intelligent-classification-in-medical-coding-with-the-mcoder-t-model/455364/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/application-of-multimodal-data-fusion-and-intelligent-classification-in-medical-coding-with-the-mcoder-t-model/455364.png","ImageObject",300,407,{"name":92,"@type":93},"ahduag","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the MCoder-T model address in medical coding?","Question",{"text":112,"@type":113},"It addresses the complexity of assigning case codes by jointly performing multimodal fusion and intelligent classification, overcoming limitations of traditional methods that handle heterogeneous sources poorly.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which data types does MCoder-T use for coding?",{"text":117,"@type":113},"MCoder-T integrates text, medical images, and structured data to improve coding automation and classification accuracy.",{"name":119,"@type":110,"acceptedAnswer":120},"What methods or mechanisms are central to MCoder-T?",{"text":121,"@type":113},"The model uses causal-to-mask attention mechanisms, integrated multimodal fusion, and multitask learning optimization.",{"name":123,"@type":110,"acceptedAnswer":124},"How does MCoder-T perform compared with traditional and other progressive models?",{"text":125,"@type":113},"Experimental results indicate MCoder-T outperforms traditional and other progressive methods, achieving an overall productivity improvement of about 7% to 18% across evaluation indicators.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},455364,1792705049,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149,"preview_page_count":4},3985747870414,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","OPEN ACCESS  \nCitation: Li Y, Zhao J, Li X, Liang S, Tang Y (2026) Application of multimodal data fusion and intelligent classification in medical coding with the MCoder-T model. PLoS One 21(1): e0338807 . [https://doi](https://doi). org/10.1371/journal.pone.0338807  \nEditor: Qingan Qiu, Beijing Institute of Technology, CHINA  \nReceived: October 13, 2025  \nAccepted: November 26, 2025  \nPublished: January 7, 2026  \nCopyright: © 2026 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData availability statement: The datasets used in this study, including the Retailrocket Recommender System Dataset and the Retail Store Inventory Forecasting Dataset, are publicly available. The data can be accessed via the following links: COVID-19  \nRESEARCH ARTICLE  \nApplication of multimodal data fusion and intelligent classification in medical coding with the MCoder-T model  \nYisheng Li, Jie Zhao*, Xinmei Li, Shanxiong Liang, Yihui Tang  \nDepartment of Medical Records Information, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China  \n* [jiezhao0009@163.com](jiezhao0009@163.com)  \nAbstract  \nAssigning case codes is a complex problem in medical data processing, which includes multimodal data fusion and intellectual classification. Traditional case coding methods often have difficulties in managing different data sources and the complexity of the content of cases. This limits their effectiveness in actual application. To solve this problem, we propose the MCoder-T model, an intelligent case coding model, causal-to-mask attention mechanisms, integrated multimodal integration, and multitask learning optimization. MCoder-T effectively improves case coding automation and classification accuracy by integrating text, medical images, and structured data. Experimental results show that the MCoder-T model outperforms traditional methods and other progressive models by several evaluation indicators, with an overall productivity improvement of 7% to 18% . The MCoder-T model enhances the automation of drop coding tasks and demonstrates reliable adaptability during multimodal data fusion and demonstrates broad application potential.  \nIntroduction  \nWith the rapid development of information technology, data processing in the healthcare industry has entered the era of big data [1 ,2] . In this context, case coding plays a crucial role as a central element of medical information management. Coding cases is not only important for normalizing the diagnosis and treatment of diseases, but is also closely linked to the financial rules of hospitals and patient management [3] . However, traditional methods of coding cases are based on manual input, which is inefficient and prone to errors. As the types of medical data continue to grow, the burden of manual coding becomes increasingly heavy. Therefore, the automated technology of coding cases based on deep learning [4 ,5] has become a hot research topic, particularly in the field of multimodal data fusion and intelligent classification [6–9] .  \nSignificant progress has been made in recent years in Automation of coding case studies [10] . Traditional rules-based coding methods, while simple and intuitive, do  \nRadiography Database: [https://www](https://www). [kaggle.com/datasets/tawsifurrahman/](kaggle.com/datasets/tawsifurrahman/)[ ](kaggle.com/datasets/tawsifurrahman/)[covid19-radiography-database](covid19-radiography-database.)[.](covid19-radiography-database.)[ ](covid19-radiography-database.)MedMNIST: [https://github.com/](https://github.com/)  \nMedMNIST/MedMNIST.  \nFunding: Self-funded Research Project of Health Commission of Guangxi Zhuang Autonomous Region (Contract No.:  \nZ-A20240728) .  \nCompeting interests: The authors have declared that no competing interests exist.  \nnot lack the flexibility and ","cbCaieRjIuwdHzjG","https://ap.wps.com/l/cbCaieRjIuwdHzjG","pdf",3046256,18,"English","# Abstract\n# Introduction\n## Background and importance of case coding\n## Limitations of traditional coding methods\n## Prior work using deep learning and multimodal fusion\n# Related approaches and research directions\n## Image-text fusion challenges\n## GCN-based multimodal fusion and feature extraction\n## Causal inference with multimodal fusion","[{\"question\":\"What problem does the MCoder-T model address in medical coding?\",\"answer\":\"It addresses the complexity of assigning case codes by jointly performing multimodal fusion and intelligent classification, overcoming limitations of traditional methods that handle heterogeneous sources poorly.\"},{\"question\":\"Which data types does MCoder-T use for coding?\",\"answer\":\"MCoder-T integrates text, medical images, and structured data to improve coding automation and classification accuracy.\"},{\"question\":\"What methods or mechanisms are central to MCoder-T?\",\"answer\":\"The model uses causal-to-mask attention mechanisms, integrated multimodal fusion, and multitask learning optimization.\"},{\"question\":\"How does MCoder-T perform compared with traditional and other progressive models?\",\"answer\":\"Experimental results indicate MCoder-T outperforms traditional and other progressive methods, achieving an overall productivity improvement of about 7% to 18% across evaluation indicators.\"}]","Application of multimodal data fusion and intelligent classification in medical coding with the MCoder-T model | PDF",1790742700,45]