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Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

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SAGE awardees

Morteza Noshad, PhD

Senior Machine Learning/Natural Language Processing Scientist, Vida Health

Dr. Morteza Noshad received his Ph.D. in computer science from the University of Michigan in 2019 where he investigated information-theoretic methods to machine learning and deep learning. At Stanford University, he was a postdoctoral research fellow and research scientist in Jonathan Chen’s lab where he applied machine learning and deep learning methods to improve medical decision-making and health-care processes.

SAGE Project: Prediction of Specialty Diagnostic Procedures for the Patients with Cognitive Impairment Diseases Using Deep Representation Learning of Electronic Health Record

Dementia is one of the major causes of mortality and morbidity in older people worldwide. However, dementia and mild cognitive impairment (MCI) are under-diagnosed. Early detection of cognitive decline may be critical to the efforts to stop dementia progression, including Alzheimer’s disease (AD) and AD-related dementias (ADRD).  Early and accurate diagnosis of such diseases can be addressed by proposing new tools and models based on the patients’ medical records. In this project we use deep representation learning of the electronic health records (EHR) to predict patients who are likely to have early symptoms of cognitive impairment related diseases and need referral to specialists for further assessment as well as recommending the necessary specialty diagnostic procedures for these patients.

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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).