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Longevity & Healthy Aging Research Consortium

Longevity & Healthy Aging Research Consortium

Longevity & Healthy Aging Research Center

Logevity and Healthy Aging
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We are using large language models to detect linguistic markers of loneliness in the electronic health records to help patients struggling with loneliness
Machine learning models are helping us detect peripheral artery disease in its early stages even in dark-skinned persons
We have shown that VR bucket-listing improves wellness in seriously ill persons by allowing them to travel virtually to their desired destinations
Machine learning can be used to both predict and detect dementia in its early stages allowing for timely treatment
Patients can practice decluttering using virtual reality that simulates their home environment and thereby better manage their hoarding disorder

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Clarifying key research methodology issues

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Call for pilot awards in open!

Keynote address:
Lloyd Minor, MD, Dean of the Stanford University School of Medicine
Dr. Periyakoil receives  an “Award of Honor” from Fiji’s Ambassador to the United States 

Center News

2024 Visionary Award by the American Academy of Hospice and Palliative Medicine (AAHPM)
Dr. VJ Periyakoil

Dr. VJ Periyakoil, Professor of Medicine and Associate Dean of Research, received the 2024 Visionary Award by the American Academy of Hospice and Palliative Medicine (AAHPM). Every five years, AAHPM calls upon its extensive membership of over 5,000 professionals to nominate visionaries who have significantly shaped the landscape of palliative care. “This program recognizes extraordinary individuals who continue to enhance the delivery of care for seriously ill patients and have brought true innovation to our field,” noted Wendy-Jo Toyama, MBA FASAE, AAHPM CEO. Dr. Periyakoil’s groundbreaking contributions at Stanford University and her tireless efforts in advancing the field have earned her this distinguished accolade.

Apply for Longevity and Healthy Aging Pilot Grants 2025-2026

Apply now for the prestigious Longevity and Healthy Aging pilot awards. The applications are due on January 10, 2025 and funding will start on 07/01/2025. Projects should focus on bio-behavioral or socio-cultural studies that will advance longevity and healthy aging for all Americans. Each award is for $40,000. All awardees will be named and recognized by the NIH/NIA through the Notice of Award. Awardees will also receive mentoring and support from the LEARN faculty.

Machine learning prediction of mild cognitive impairment and its progression to Alzheimer’s disease
Machine learning AI

Health Science Report: Effective screening for mild cognitive impairment (MCI) as a risk factor for developing Alzheimer’s disease is a crucial step in helping aging population with their needs Early detection and automated screening for MCI and dementia could offer opportunities for deliberate study and recruitment into trials for developing other potentially useful therapeutics or interventions.

The study was first published in October 2023. The results illustrate that it is possible to predict MCI onset and AD progression with moderate levels of accuracy, which suggests an opportunity for population-wide screening mechanisms to identify patients at potential risk, who could then undergo more specific evaluation to consider early treatment or recruitment into clinical trials.

Longevity Medicine and Healthy Aging

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Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding"

Julia Adler-Milstein, PhD1; Jonathan H. Chen, MD, PhD; Gurpreet Dhaliwal, MD

Improving the diagnostic process is a quality and safety priority.With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).