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The approach reconciles over 1,500 peer-reviewed papers into a computational BHN model coupled with a platform that generates a simulated population of virtual individuals. Bayesian methods create digital twins from anonymized patient data, estimating risks for mild cognitive impairment and dementia with age, and enabling personalization of lifestyle interventions, off-label drugs, and supplements. Iterative refinement and a user interface support evaluation of ranked combination therapies within clinical workflows and electronic health record integration.",{"@graph":69,"@context":122},[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/development-of-digital-twin-clinical-decision-support-tool-for-alzheimers-prediction-and-prevention-proceedings/427950/",{"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/development-of-digital-twin-clinical-decision-support-tool-for-alzheimers-prediction-and-prevention-proceedings/427950.png","ImageObject",300,407,{"name":92,"@type":93},"นรินทร์","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How are digital twins created for predicting Alzheimer’s outcomes?","Question",{"text":112,"@type":113},"Bayesian methods use data from individuals in a clinic or trial setting to create digital twins, enabling simulation of predicted risk over age.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What kinds of interventions can the tool evaluate?",{"text":117,"@type":113},"It performs preliminary assessments of lifestyle interventions as well as off-label drugs and supplements, aiming to personalize them for extending normal cognition.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the project plan to fit into real clinical workflows?",{"text":121,"@type":113},"A roadmap includes integration into electronic health record systems and clinical workflows, supported by iterative prototype refinement and a user interface for reviewing results.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},427950,1790742098,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":81},2336475104957,"https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136","Innovation in Aging, 2025, Vol. 9, No. S2 941  \nthe clinical workflow operated, in addition to feedback on the presented prototype. Anonymized patient cases were used to create digital twins and discuss how the results could be utilized. This process resulted in a roadmap for development including integration into electronic health record systems and clinical workflows.  \nAbstract citation ID: igaf122.3178  \nDEVELOPMENT OF DIGITAL TWIN CLINICAL  \nDECISION SUPPORT TOOL FOR ALZHEIMER’S  \nPREDICTION AND PREVENTION  \nAlex Bangs1, Thomas Paterson2, Jennifer Rohrs2, Don Breuner2, and Cory Funk2, 1. Fulcrum Neuroscience, Inc., Minnesota, United States, 2. Fulcrum Neuroscience, Inc., Palo Alto, California, United States  \nThe Fulcrum Brain Health and Neurodegeneration (BHN) model is the product of reconciling over 1,500 peer reviewed papers and accompanying data sets. Our model is coupled with a computational software platform to create a simulated population of millions of virtual individuals representing genetic and environmental variations observed in clinical and observational studies. Using a Bayesian approach, data from individuals in a clinic or trial setting are used to create digital twins. Each digital twin can simulate the predicted risk of mild cognitive impairment  \n(MCI) and dementia with age. Additionally, we have performed preliminary assessments of a set of established interventions around lifestyle interventions, off-label drugs and supplementsand how they can be personalized to extend normal cognition.  \nOur digital twins can be further evaluated for these combination therapies, to provide a rank-ordered set of intervention combinations that are predicted to be most effective. We’ve developed a user interface for reviewing digital twin results, starting with a static, graphical prototype and then a running prototype able to create digital twins based on user-provided data. We refined these prototypes following interviews with gerontologists, neurologists, neuropsychologists, and primary care physicians.  \nInterviews included discovery questions about how cognitive assessments were done, how treatments were selected, and how","cbCaii9uUdxOkI1v","https://ap.wps.com/l/cbCaii9uUdxOkI1v","pdf",46950,"English","# Development of Digital Twin Clinical Decision Support Tool for Alzheimer’s Prediction and Prevention\n## Clinical model and Bayesian twin creation\n## Intervention personalization and ranked combination evaluation\n## Prototype user interface and workflow integration","[{\"question\":\"How are digital twins created for predicting Alzheimer’s outcomes?\",\"answer\":\"Bayesian methods use data from individuals in a clinic or trial setting to create digital twins, enabling simulation of predicted risk over age.\"},{\"question\":\"What kinds of interventions can the tool evaluate?\",\"answer\":\"It performs preliminary assessments of lifestyle interventions as well as off-label drugs and supplements, aiming to personalize them for extending normal cognition.\"},{\"question\":\"How does the project plan to fit into real clinical workflows?\",\"answer\":\"A roadmap includes integration into electronic health record systems and clinical workflows, supported by iterative prototype refinement and a user interface for reviewing results.\"}]","Development of Digital Twin Clinical Decision Support Tool for Alzheimer’s Prediction and Prevention - Proceedings | PDF",1790645267]