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Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

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SAGE awardees

Alesha Heath, PhD

Postdoctoral Research Fellow
Department of Psychiatry and Behavioral Sciences

Dr. Alesha Heath is a Postdoctoral Scholar at Psychiatry and Behavioral Sciences at Stanford School of Medicine and the MIRECC the VA Palo Alto. She earned her PhD from the both the University of Western Australia and Sorbonne University in which she investigated how brain stimulation, in particular repetitive transcranial magnetic stimulation (rTMS), can be tailored to different disorders. At Stanford, Dr. Heath’s postdoctoral studies will continue to investigate mechanisms of brain stimulation however she is also involved in implementing rTMS treatment to patients with Mild Cognitive Impairment. Her research also has a strong focus on biomarkers and genetics of Alzheimer’s disease and the ability to predict the response to treatment using these biomarkers.
https://profiles.stanford.edu/alesha-heath

SAGE Project: Identifying Variable Number Tandem Repeats Associated with Alzheimer’s Disease in Diverse Populations

Dr Heath’s study will examine the association of Variable Number Tandem Repeats (VNTRs), a genetic polymorphism, in an Alzheimer’s disease (AD) population. In the past VNTRs have been an overlooked polymorphic component of the genome even though a number of VNTRs are associated with neurological disorders and brain functions. Using a specialized program, VNTRseek, this study will perform a first of its kind exploratory analysis of these VNTRs in the whole genome sequences from Alzheimer’s disease cases and controls. This research will prioritize investigation into ethno-racial groups that are often underrepresented in AD genetics research by analyzing VNTRs across a diverse population sample. Thereby understanding the association of various VNTRs to AD but also how different populations influences the relationship of these VNTRs to Alzheimer’s disease. This will inform new genetic profiles that are applicable across diverse populations or may be specific to different ethno-racial backgrounds.

 

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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).