This Project Grant award from the National Institute on Aging (CFDA 93.866 - Aging Research) provides $129,330 to The General Hospital Corporation (Massachusetts General Hospital) to conduct research on profiling and forecasting individual progression in Alzheimer's disease and related dementias (ADRD) using machine learning. The key products and services to be delivered under this 5-year award include: Deploying a multi-modal information framework that leverages decades-long in vivo and digital...
This Project Grant award of $353,391.00, provided by the National Institute on Aging (CFDA 93.866 - Aging Research), aims to validate the use of a "Personal Learning Curve" paradigm as a sensitive marker of early memory decline related to Alzheimer's disease (AD) biomarkers in a large, diverse sample of older adults. The study seeks to determine if this digital cognitive assessment tool, which measures learning over repeated evaluations, can detect subtle AD-related memory failures...
This Project Grant award from the National Institute on Aging (Aging Research, CFDA 93.866) supports a $7,997,945 initiative to improve the diagnosis of Alzheimer's disease and related dementias in primary care settings. The project, led by the University of California, San Francisco, aims to demonstrate that a comprehensive diagnostic toolkit, combined with expert-led case conferences, can help primary care providers accurately distinguish mild cognitive impairment and Alzheimer's-related...
This Project Grant award of $4,787,820.00 from the National Institute on Aging (CFDA 93.866 - Aging Research) aims to gain a better understanding of the individual- and system-level characteristics that influence diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease and related dementias (ADRD), and how diagnosis can moderate the effects of cognitive decline on employment, future care, and quality of life. The key objectives are to: 1) examine whether older adults experiencing...
This Project Grant award of $2,683,965 from the National Institute of Neurological Disorders and Stroke (NINDS) under the Extramural Research Programs in the Neurosciences and Neurological Disorders (CFDA 93.853) will support the development and optimization of an improved all-human cellular system, or "brain-chip", for studying Alzheimer's disease and related dementias (ADRDs). The key objectives are to: 1) define delivery conditions for introducing patient blood cells and plasma into...
This Project Grant award from the National Institute on Aging (CFDA 93.866 Aging Research) provides $885,006 to Brigham & Women's Hospital Inc. to develop a smartphone-based point-of-care diagnostic system for detecting Alzheimer's disease-related biomarkers in blood plasma. The system will utilize microfluidics, nanotechnology, optical sensing, and deep learning to measure amyloid-beta, phosphorylated tau, and neurofilament light chain within 30 minutes, with the goal of creating an...
This Project Grant award from the National Institute on Aging (CFDA 93.866 - Aging Research) provides $778,499 to the University of Texas Health Science Center at Houston to develop novel machine learning models to reveal and stratify heterogeneous subpopulations of Alzheimer's disease (AD) patients based on risk and progression patterns. The goal is to transform these subpopulations into potentially targetable groups to enable focused clinical trials, support future therapeutic development, and...
This four-year $1.15 million Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) supports research at the University of Michigan to develop techniques for learning from longitudinal observational clinical data in the presence of noise and confounding to tackle progressive diseases. The University will create shareable electronic health record-based definitions of mild cognitive impairment and Alzheimer's disease, and develop...
The National Institute on Aging (NIA) awarded a Project Grant (CFDA 93.866 - Aging Research) in the amount of $661,016 to the University of Southern California (USC) to develop a "Federated Deep Learning to Accelerate Alzheimer's Disease Research" project. The key products and services to be delivered include: Establishing an international "Federate AD" alliance to leverage innovative AI techniques and distributed computation to study Alzheimer's disease (AD) across large,...
This $1,505,109 Project Grant awarded by the National Institute on Aging (NIA), under the Aging Research federal grant program (CFDA 93.866), aims to identify undiagnosed Alzheimer's disease (AD) in non-Hispanic White, Hispanic/Latino, and non-Hispanic African American populations. The project will leverage electronic health records and genetic data to develop disease trajectory models and polygenic risk scores to detect undiagnosed AD, while addressing the issue of underdiagnosis in underserved...
CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DETECTION OF COGNITIVE DECLINE USING ELECTRONIC HEALTH RECORDS AND DEEP LEARNING - PROJECT SUMMARY THE PREVALENCE OF ALZHEIMER'S DISEASE (AD) AND RELATED DEMENTIA (AD/ADRD) IS EXPECTED TO NEARLY TRIPLE TO A STAGGERING 13 MILLION AFFECTED AMERICANS AND THE TOTAL COSTS OF CARE ARE PROJECTED TO INCREASE FIVE-FOLD TO 1.1 TRILLION DOLLARS BY THE YEAR 2050. EARLY DETECTION OF PRECURSOR STAGES OF AD/ADRD BECOMES EXTREMELY IMPORTANT, AS IT CAN INTRODUCE TREATMENT OR INTERVENTION EARLIER FOR POTENTIAL AD/ADRD PATIENTS, GIVEN EXISTING TREATMENTS ONLY HAVE MODEST BENEFIT AT BEST. EARLY COGNITIVE DECLINE OF PATIENTS IS OFTEN UNDER DIAGNOSED BY PRIMARY CARE PHYSICIANS (PCPS). A CLINICAL DECISION SUPPORT (CDS) TOOL THAT CAN AUTOMATICALLY DETECT COGNITIVE DECLINE SIGNALS FROM LONGITUDINAL ELECTRONIC HEALTH RECORDS (EHRS) AND FACILITATE PCPS TO MAKE TIMELY DIAGNOSES WOULD BE HIGHLY DESIRABLE, AS IT WOULD RESULT IN EARLY INTERVENTION FOR POTENTIAL AD/ADRD PATIENTS. IN OUR PHASE I EQUIVALENT WORK AT HARVARD MEDICAL SCHOOL, WE HAVE DEVELOPED A DEEP LEARNING MODEL FOR EARLIER DETECTION OF COGNITIVE DECLINE USING CLINICAL NOTES IN MASS GENERAL BRIGHAM'S EHRS. HERE WE PROPOSE A DIRECT-TO-PHASE II STUDY, WHICH FURTHER DEVELOPS NOVEL DEEP LEARNING ALGORITHMS FOR THE EARLY DETECTION OF COGNITIVE DECLINE, IMPLEMENT THEM INTO A CLINICAL DECISION SUPPORT TOOL, AND VALIDATE THE TOOL IN A PRIMARY CARE SETTING. SPECIFICALLY, IN AIM 1, WE WILL DEVELOP NOVEL ONTOLOGY, NLP, AND CLASSIFICATION APPROACHES TO IDENTIFY PATIENTS WITH EARLY COGNITIVE DECLINE USING RECORDS FROM EHR AND EXTRACT RELATED EVIDENCE FROM CLINICAL NOTES. IN AIM 2, WE WILL WORK WITH FRONTLINE PHYSICIANS TO DESIGN, DEVELOP AND EVALUATE A USER-CENTERED CLINICAL DECISION SUPPORT TOOL TO IDENTIFY AND MANAGE PATIENTS WITH COGNITIVE DECLINE. THE SYSTEM, WHICH WE INTEND TO ALIGN WITH EVIDENCE-BASED FRAMEWORKS SUCH AS THE CMS COLLABORATIVE CARE MODEL, WILL IDENTIFY PATIENTS AT RISK (WITH SUPPORTING EVIDENCE) AND PROMPT PERSONALIZED RECOMMENDATIONS FOR TIMELY CARE. ONCE THE SYSTEM IS DEVELOPED AND FULLY TESTED, WE WILL IMPLEMENT THE DEVELOPED CDS TOOL IN A SIMULATED EHR ENVIRONMENT AT MASS GENERAL BRIGHAM HEALTHCARE SYSTEM, USING REAL PATIENT DATA, AND FORMALLY EVALUATE ITS UTILITY AND USABILITY BY RECRUITING PRIMARY CARE CLINICIANS. THIS PROJECT WILL DELIVER NOT ONLY EFFECTIVE MODELS FOR EARLY DETECTION OF COGNITIVE DECLINE, BUT ALSO A PRACTICAL AND VALIDATED CDS TOOL THAT CAN IMPROVE DIAGNOSIS OF PRECURSOR STAGES OF AD/ADRD, THUS FACILITATING EARLY INTERVENTION FOR POTENTIAL AD/ADRD PATIENTS. IF SUCCESSFUL, IT WILL BE THE FIRST STUDY THAT ENGAGES PRIMARY CARE PHYSICIANS AND REAL PATIENT DATA TO VALIDATE THE UTILITY OF SUCH A COGNITIVE DECLINE DETECTION TOOL.