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Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

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SAGE awardees

Róbert Pálovics, PhD

Postdoctoral Research Fellow, Neurology and Neurological Sciences

Róbert Pálovics is a Postdoctoral Scholar at Neurology and Neurological Sciences at Stanford School of Medicine. He received his Ph.D. in computer science at the University of Technology and Economics in Hungary.

His research leverages data from large-scale biological studies describing organisms at the cellular level and uses machine learning (ML), statistical methods and network science to understand aging and neurodegeneration. He seeks to understand the functional changes with aging starting from the cell level and search for signatures that will serve as the basis for rejuvenation and interventions to prevent or delay age-related diseases. Róbert’s research is highly interdisciplinary and has made significant contributions to both computer science and to neuroscience. His research in aging has been published in multiple Nature articles and his work in computer science has been published in premier machine learning and data mining venues such as the International Conference on Machine Learning.

SAGE Project: Developing machine learning methods to infer clinical measures of cognitive impairment from single-cell transcriptomics data

Cognitive impairment is often not diagnosed in early stages of the dementia trajectory. Patients from ethnic minorities are particularly vulnerable to the huge burdens imposed by dementia due to their lesser access to quality care. In our project we will make use of the rich multimodal data available in the Stanford Alzheimer’s Disease Research Center of Latinx American and Caucasian participants to pave the way for the early diagnosis of cognitive impairment. We intend to utilize robust machine learning models to associate transcriptomic signatures from next-generation single-cell RNA-sequencing data with baseline measures of cognitive impairment.

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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).