[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"sub-optimal-health\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:sub-optimal-health":24},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":13,"acronym":4,"eligibilityCriteria":14,"healthyVolunteers":15,"sex":16,"minAge":4,"maxAge":4,"enrollmentInfo":17,"targetDuration":4,"studyType":20,"phases":4,"briefSummary":21,"conditions":22,"keywords":25,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":32,"lastUpdatePostDateStruct":33,"startDateStruct":36,"completionDateStruct":38,"leadSponsor":40,"locationsCount":5},"100643367","real-world-data-linkage-research-platform-100643367",false,"NCT07635355","Real-World Data Linkage Research Platform","Inclusion Criteria:\n\n* Participants will be eligible for inclusion if they meet all of the following criteria:\n\n  1. Availability of any health-related data generated from routine clinical care, health examinations, or disease surveillance systems, regardless of disease type or health status.\n  2. Presence of at least one type of usable data, including but not limited to diagnostic information (structured or unstructured), laboratory results, imaging data, or basic demographic information.\n  3. Records contain sufficient information (appropriately anonymized) to allow data organization and, where feasible, linkage at the individual level across time points or data sources.\n\nExclusion Criteria:\n\n* Participants or records meeting any of the following criteria will be excluded:\n\n  1. Records lacking minimal essential information required to distinguish individual records or support basic analysis (e.g., completely missing identifiers or time information).\n  2. Records confirmed to be invalid, including system-generated test data, corrupted entries, or records that do not represent real clinical or health-related events.\n  3. Exact duplicate records that cannot be resolved through standard data processing (only one record will be retained when duplicates are identifiable).",true,"ALL",{"count":18,"type":19},300000,"ESTIMATED","OBSERVATIONAL","This study aims to address the lack of intelligent governance tools in clinical data management to promote efficient governance and secure sharing of real-world health data. To achieve this, a self-adaptive, automated governance intelligent agent will be developed based on a High-Order Programming (HOP) architecture, integrating Large Language Models (LLMs) and deep learning techniques. The agent will continuously monitor and correct data quality issues in real time, improving data accuracy and usability.\n\nIn parallel, the project will establish a trusted data-sharing framework by integrating AI Confidential Computing (AICC) with Trusted Data Matrix (TDM) technologies. This framework will enable secure, real-time cross-institutional data exchange and collaborative computation while protecting sensitive information.\n\nOverall, the study aims to transform fragmented clinical data into high-quality, standardized, and securely accessible resources, thereby facilitating the circulation of data value and advancing collaborative medical research.",[23,24],"Chronic Diseases","Sub-optimal Health",[26,27,28,29,30],"Real-world data","Intelligent data governance","confidential computation","trusted data matrix","chronic diseases","NOT_YET_RECRUITING","2026-06-03",{"date":34,"type":35},"2026-06-09","ACTUAL",{"date":37,"type":19},"2026-05-30",{"date":39,"type":19},"2030-12-31",{"name":41,"class":42},"Beijing Friendship Hospital","OTHER"]