[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121654-en":3,"doc-seo-121654-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121654,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Modeling and Analytics of Multi-factor Disease Evolutionary Process by Fusing Petri Nets and Machine Learning Methods","Recent advances in medical informatization increasingly rely on machine learning and evolutionary computation, yet effective approaches for studying multi-factor disease evolutionary processes remain insufficient. A key challenge is the lack of visual, multifactor evolution models and analysis methods that can both represent disease progression and quantify factor importance. This work proposes a fused framework combining Coloured Petri nets with machine learning to provide a clear process visualization and efficient data analytics. Feasibility is demonstrated by modeling monoamine hormones in depression to output pathogenic probabilities under different hormone levels, highlighting the roles of distinct factors.","Modeling and Analytics of Multi-factor Disease Evolutionary Process by Fusing Petri Nets and Machine Learning Methods  \nWangyang Yua,b , Xuyue Wanga,b,∗ , Xianwen Fangc and Xiaojun Zhaid  \na Key Laboratory of Intelligent Computing and Service Technology for Folk Song, Ministry of Culture and Tourism, Shaanxi Normal University, No. 620, West Chang’an Street, Chang’an District, Xi’an, 710119, China  \nb School of Computer Science, Shaanxi Normal University, No. 620, West Chang’an Street, Chang’an District, Xi’an, 710119, China c Department of Computer Science and Engineering, Anhui University of Science and Technology, An’hui, 232001, China  \nd School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, U.K.  \nARTICLE INFO  \nKeywords: Evolutionary Process Machine Learning Data Analytics Multifactorial Disease Coloured Petri nets  \nAB STRACT  \nRecent years, informatization methods have been gradually applied to medical treatment, in which machine learning and evolutionary computation play an important role. However, the effective methods for the study of multi-factor disease evolutionary process are still largely open. There are some issues in the field of disease analysis, such as the lack of visual multifactor disease evolution model and effective analysis methods. For a universal method of data analysis and medical diagnosis, the machine learning algorithms should be combined with the formal modeling methods to fully realize the complementary advantages, make model has the advantages of visualization and efficient data analysis. This work proposes a novel research idea for the modeling analysis of current multi-factor diseases and reveal its feasibility, so as to explore potential pharmaceutical targets and enable doctors and patients to better understand the evolution process of multi-factor diseases. It is worth mentioning that, in order to verify the feasibility of the proposed idea, we applied it to the analysis of the role of monoamine hormones in depression. The model incorporates the machine learning algorithms, and it finally outputs the pathogenic probability under different hormone levels, reflecting the importance of different factors on depression. The application case proved that we provide a clear process model and a novel research method for multi-factor disease evolutionary process analysis.  \n1. Introduction  \nThe advancement of Internet technology has led to an increase in the application of research methods in the medical field [1, 2, 3, 4, 5] . However, the complexity of disease development is significant. The interaction of external factors and the body’s reaction functions can result in changes in the structure, metabolism, and function of the body. Understanding these changes is essential for studying and preventing diseases [6] . The field of pathology is critical in identifying the causes, mechanisms, and rules of disease development [7, 8, 9, 10] . In addition, research on multi-factor disease analysis has been conducted, but currently, there is a lack of a suitable method for visualizing the evolutionary process of diseases, specifically in terms of displaying the impact of disease and the probability of different factors. To address this, we propose a method that combines formal methods and machine learning algorithms for multi-factor disease evolutionary process analysis. This approach aims to resolve the non-visual aspect of disease progression and aid doctors in analyzing and observing the effects of various factors on disease development.  \nIn this paper, in order to facilitate the model construction, we firstly proposed a generalized model of multi-factor disease analysis using Coloured Petri nets (CPN) [11], and then used the refined the original generalized model through CPN for specific diseases. The advantages and progressiveness of this model are mainly reflected in the visualization of the process, and innovatively incorporating machine learning algorit","cbCair8hpXmPfAtj","https://ap.wps.com/l/cbCair8hpXmPfAtj","pdf",624641,1,15,"English","en",105,"# Introduction\n## Motivation for visual multifactor disease evolution analysis\n## Proposed combined approach: formal modeling and machine learning","[{\"question\":\"What problem does the paper aim to solve for multi-factor disease analysis?\",\"answer\":\"It addresses the lack of visual models for disease evolutionary processes and the absence of effective analysis methods that can display how different factors influence progression and probability.\"},{\"question\":\"How do Coloured Petri nets and machine learning work together in the proposed method?\",\"answer\":\"Coloured Petri nets are used to construct and visualize the multi-factor disease evolutionary process, while machine learning processes data to output disease pathogenic probability and quantify factor influence.\"},{\"question\":\"Why is depression chosen as the application case?\",\"answer\":\"Depression has a high global incidence, and the pathological role and relationships among monoamine hormones are not clearly presented in graph form, making it suitable to demonstrate the method’s feasibility.\"}]","Modeling and Analytics of Multi-factor Disease Evolutionary Process by Fusing Petri Nets and Machine Learning Methods | 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problem does the paper aim to solve for multi-factor disease analysis?","Question",{"text":75,"@type":76},"It addresses the lack of visual models for disease evolutionary processes and the absence of effective analysis methods that can display how different factors influence progression and probability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do Coloured Petri nets and machine learning work together in the proposed method?",{"text":80,"@type":76},"Coloured Petri nets are used to construct and visualize the multi-factor disease evolutionary process, while machine learning processes data to output disease pathogenic probability and quantify factor influence.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is depression chosen as the application case?",{"text":84,"@type":76},"Depression has a high global incidence, and the pathological role and relationships among monoamine hormones are not clearly presented in graph form, making it suitable to demonstrate the method’s 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