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OpenClinicalAI: An open and dynamic model for Alzheimer's Disease diagnosis

文献类型：期刊论文

作者Huang, Yunyou1,3,6; Liang, Xiaoshuang1,3; Xie, Jiyue1,3; Lu, Xiangjiang1,3; Miao, Xiuxia1,3; Liu, Wenjing1,3,5; Zhang, Fan2; Kang, Guoxin2,4; Ma, Li5; Tang, Suqin1
刊名EXPERT SYSTEMS WITH APPLICATIONS
出版日期2025-02-01
卷号261页码:17
关键词Real-world clinical setting Alzheimer's disease Diagnose AI Deep learning
ISSN号0957-4174
DOI10.1016/j.eswa.2024.125528
英文摘要Although Alzheimer's disease (AD) cannot be reversed or cured, timely diagnosis can significantly reduce the burden of treatment and care. Current research on AD diagnosis models usually regards the diagnosis task as a typical classification task with two primary assumptions: (1) All target categories are known a priori; (2) The diagnostic strategy for each patient is consistent, that is, the number and type of model input data for each patient are the same. However, real-world clinical settings are open, with complexity and uncertainty in terms of both subjects and the resources of the medical institutions. This means that diagnostic models may encounter unseen disease categories and need to dynamically develop diagnostic strategies based on the subject's specific circumstances and available medical resources. Thus, the AD diagnosis task is tangled and coupled with the diagnosis strategy formulation. To promote the application of diagnostic systems in real- world clinical settings, we propose OpenClinicalAI for direct AD diagnosis in complex and uncertain clinical settings. This is the first end-to-end model to dynamically formulate diagnostic strategies and provide diagnostic results based on the subject's conditions and available medical resources. OpenClinicalAI combines reciprocally coupled deep multi-action reinforcement learning (DMARL) for diagnostic strategy formulation and multicenter meta-learning (MCML) for open-set recognition. The experimental results show that OpenClinicalAI achieves better performance and fewer clinical examinations than the state-of-the-art model. Our method provides an opportunity to embed the AD diagnostic system into the current healthcare system to cooperate with clinicians to improve current healthcare.
资助项目Standardization Research Project of Chinese Academy of Sciences[BZ201800001] ; Project of Guangxi Science and Technology[GuiKeAD20297004] ; National Natural Science Foundation of China[61967002] ; National Natural Science Foundation of China[U21A20474]
WOS研究方向Computer Science ; Engineering ; Operations Research & Management Science
语种英语
WOS记录号WOS:001343509200001
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/39515]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Tang, Suqin; Zhan, Jianfeng
作者单位1.Guangxi Normal Univ, Key Lab Educ Blockchain & Intelligent Technol, Minist Educ, Guilin, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Guilin Med Univ, Guilin, Peoples R China
6.Int Open Benchmark Council, Delaware, CA USA
推荐引用方式
GB/T 7714		 Huang, Yunyou,Liang, Xiaoshuang,Xie, Jiyue,et al. OpenClinicalAI: An open and dynamic model for Alzheimer's Disease diagnosis[J]. EXPERT SYSTEMS WITH APPLICATIONS,2025,261:17.
APA Huang, Yunyou.,Liang, Xiaoshuang.,Xie, Jiyue.,Lu, Xiangjiang.,Miao, Xiuxia.,...&Zhan, Jianfeng.(2025).OpenClinicalAI: An open and dynamic model for Alzheimer's Disease diagnosis.EXPERT SYSTEMS WITH APPLICATIONS,261,17.
MLA Huang, Yunyou,et al."OpenClinicalAI: An open and dynamic model for Alzheimer's Disease diagnosis".EXPERT SYSTEMS WITH APPLICATIONS 261(2025):17.

入库方式： OAI收割

来源：计算技术研究所

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