[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100576914":3},{"organization":4,"armGroups":7,"interventions":18,"overallOfficials":10,"centralContacts":23,"locations":29,"responsibleParty":79,"collaborators":10,"id":84,"slug":85,"hasResults":86,"nctId":87,"briefTitle":88,"officialTitle":89,"acronym":10,"eligibilityCriteria":90,"healthyVolunteers":91,"sex":92,"minAge":93,"maxAge":94,"enrollmentInfo":95,"targetDuration":10,"studyType":98,"phases":10,"briefSummary":99,"conditions":100,"keywords":102,"overallStatus":32,"whyStopped":10,"lastUpdateSubmitDate":106,"lastUpdatePostDateStruct":107,"startDateStruct":110,"completionDateStruct":112,"leadSponsor":113,"locationsCount":114},{"fullName":5,"class":6},"The Eye Hospital of Wenzhou Medical University","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Biologically Younger Group",null,"Participants whose biological age is predicted to be younger than their chronological age.",[13],"Other: AI-assisted predictive model",{"label":15,"type":10,"description":16,"interventionNames":17},"Biologically Older Group","Participants whose biological age is predicted to be older than their chronological age.",[13],[19],{"type":6,"name":20,"description":21,"armGroupLabels":22,"otherNames":10},"AI-assisted predictive model","This study utilizes an AI-assisted predictive model that analyzes multimodal data from electronic health records, including medical history, laboratory results, imaging data, and lifestyle factors, to estimate biological age. The model employs deep learning algorithms to predict biological age, compare it to chronological age, and identify early signs of age-related health risks. The intervention is not a direct treatment or procedure but aims to develop a tool for predicting biological age to help personalize care and improve long-term health outcomes.",[15,9],[24],{"name":25,"role":26,"phone":27,"phoneExt":10,"email":28},"Fei Liu, MD","CONTACT","+86 13810512704","liufei_2359@163.com",[30,48,61,71],{"facility":31,"status":32,"city":33,"state":34,"zip":10,"country":35,"countryCode":36,"cosmosGeoPoint":37,"geoPoint":42,"contacts":43},"Nanfang Hospital","RECRUITING","Guangzhou","Guangdong","China","CN",{"type":38,"coordinates":39},"Point",[40,41],113.25,23.11667,{"lat":41,"lon":40},[44],{"name":45,"role":26,"phone":46,"phoneExt":10,"email":47},"Zhuomin Li","+86-0577-85397527","chetneyli.1001@gmail.com",{"facility":49,"status":32,"city":50,"state":51,"zip":10,"country":35,"countryCode":36,"cosmosGeoPoint":52,"geoPoint":56,"contacts":57},"First Affiliated Hospital of Wenzhou Medical University","Wenzhou","Zhejiang",{"type":38,"coordinates":53},[54,55],120.66682,27.99942,{"lat":55,"lon":54},[58],{"name":59,"role":26,"phone":10,"phoneExt":10,"email":60},"Cheng Tang","c249325687@163.com",{"facility":62,"status":32,"city":50,"state":51,"zip":10,"country":35,"countryCode":36,"cosmosGeoPoint":63,"geoPoint":65,"contacts":66},"Second Affiliated Hospital of Wenzhou Medical University",{"type":38,"coordinates":64},[54,55],{"lat":55,"lon":54},[67],{"name":68,"role":26,"phone":69,"phoneExt":10,"email":70},"Sian Liu","+86-0577-88002888","liusan@mail3.sysu.edu.cn",{"facility":5,"status":32,"city":50,"state":51,"zip":10,"country":35,"countryCode":36,"cosmosGeoPoint":72,"geoPoint":74,"contacts":75},{"type":38,"coordinates":73},[54,55],{"lat":55,"lon":54},[76],{"name":77,"role":26,"phone":46,"phoneExt":10,"email":78},"Lan Wang","wl2832300533@163.com",{"type":80,"investigatorFullName":81,"investigatorTitle":82,"investigatorAffiliation":83,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Kang Zhang","Chief Scientist","Wenzhou Medical University","100576914","ai-driven-prediction-of-biological-age-with-ehr-100576914",false,"NCT06791486","AI-Driven Prediction of Biological Age With EHR","Predicting Biological Age Using Electronic Health Records: An AI-Based Approach","Inclusion Criteria:\n\n1. Patients with comprehensive and accessible EHR data, including medical history, laboratory results, treatment data, imaging data (if available), and lifestyle factors (e.g., smoking, physical activity, diet).\n2. Patients with no significant cognitive impairments that would prevent them from providing informed consent or participating in the study.\n3. All participants must provide informed consent for the use of their medical data for research purposes.\n\nExclusion Criteria:\n\n1. Patients with incomplete or missing critical EHR data such as medical history, laboratory results, or treatment data that are necessary for predicting biological age.\n2. atients with severe cognitive disorders (e.g., dementia, significant mental disabilities) who are unable to provide informed consent or participate meaningfully in the study.\n3. Patients with terminal illnesses or those with limited life expectancy where biological age predictions may not be relevant for the purposes of the study.",true,"ALL","0 Years","100 Years",{"count":96,"type":97},1000000,"ESTIMATED","OBSERVATIONAL","This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for predicting biological age using electronic health records (EHR). The study will analyze various health data points, including medical history, laboratory results, and clinical observations, to estimate the biological age of patients. By comparing biological age with chronological age, the study aims to assess the accuracy of the model and its potential in identifying age-related health risks and improving patient care.",[101],"Biological Age",[101,103,104,105],"electronic health records","AI prediction","aging","2025-04-01",{"date":108,"type":109},"2025-04-02","ACTUAL",{"date":111,"type":109},"2023-03-01",{"date":108,"type":97},{"name":5,"class":6},4]