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Author Archives: DBDS Admin

Serena Yeung
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DBDS faculty featured prominently in cover story of latest SOM Magazine: “Medicine’s AI Boom: The Stanford Impact”

Eleven of DBDS’ faculty and associated faculty were featured in the latest issue of Stanford Medicine Magazine.
The Stanford Impact” focused on a wide scope of researchers that impact the AI work done in DBDS, including Nigam Shah, Serena Yeung (pictured), Roxana Daneshjou, Tina Hernandez-Boussard and James Zou.
“If you poll the 1,200 faculty in the School of Medicine, I’d be surprised if more than 10% know about any of Stanford’s AI history,” said Shah, MBBS, PhD, professor of medicine and of biomedical data science and chief data scientist for Stanford Health Care. “A lot of people think this right now is the first AI hype cycle,” the story quoted, and went on to include how AI is defined and in which ways Stanford is taking the lead in the field.
Manuel Rivas
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Weekly Seminar: Manny Rivas, “Prediction and inference from population scale datasets” 11/16

Prediction and inference from population scale datasets

Thursdays 11/16 1:30-3:00 pm in MSOB x303

Population biobanks are a valuable resource for identifying genetic and environmental factors that contribute to disease. Recent advances in statistical methods and computational power have enabled the analysis of large-scale datasets from these biobanks, leading to the discovery of novel therapeutic targets and pathways. This seminar will present on the use of population biobank scale datasets for the analysis of renal, liver, and sex hormone biomarkers. In addition, I will discuss the path from statistical methodological development to target identification for glaucoma to therapeutic development using monoclonal antibodies to mimic effects of protective mutations in humans. Finally, I will present on approaches for disease risk prediction using genetics, metabolomics, and proteomics data. Together, the methods and applications presented in this talk demonstrate the value of population-scale cohorts to advance our understanding of disease and development of new treatments.

Suggested reading:

Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma, https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008682

Genetics of 35 blood and urine biomarkers, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867639/

Tanigawa Y, Qian J, Venkataraman G, Justesen JM, Li R, Tibshirani R, et al. (2022) Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLoS Genet 18(3): e1010105. https://doi.org/10.1371/journal.pgen.1010105

Bayesian model comparison for rare-variant association studies
GR Venkataraman, C DeBoever, Y Tanigawa… – The American Journal of Human Genetics, 2021. Julia Carrasco-Zanini,et al.
MedRxiv 2023.07.18.23292811; doi:https://doi.org/10.1101/2023.07.18.23292811

This weekly seminar (running during Fall, Winter and Spring quarters) doubles as a class “Workshops in Biostatistics (BIODS/STATS 260).”

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Dr. Richard Olshen, pioneer in field of statistics: 1942-2023

Dear DBDS,
It is with deep sorrow that I write to inform you of the passing of Professor Richard Allen Olshen on November 8, 2023.
Professor Olshen will be forever remembered for his pioneering research in the field of statistics and his unyielding passion for academia, especially to our shared mission at Stanford University. Richard played a significant role in advancing statistical learning, notably through his influential work on regression and classification trees. His internationally acclaimed and pioneering work spanned various domains, including gait analysis, digital radiography, and, in more recent years, molecular genetics. As the division chief of Biostatistics in the Department of Health Research and Policy, his leadership, especially in the shaping of biostatistics at Stanford, will be fondly remembered. Professor Olshen was a key figure in the development of the Biostatistics Workshop (predecessor to the weekly department seminar series in DBDS) and Data Studio. His many contributions will leave a lasting imprint on our School of Medicine and Stanford University.
Richard Olshen was born in Portland, Oregon, on May 17, 1942. After completing his undergraduate training at UC Berkeley, he attended Yale University, where he earned his Ph.D. in Statistics in 1966 under the guidance of L.J. Savage. In 1967, he came to Stanford University, marking the beginning of a lifelong connection with our institution. In 1975, Professor Olshen left the Stanford family to join the faculty of the Mathematics Department at UC San Diego. However, his heart remained with Stanford, and he returned in 1989.
Richard Olshen was a passionate mentor to many. In addition to his academic accomplishments, Richard was also a dedicated husband, nurturing father, and doting grandfather. Although we deeply mourn his loss, his influence on our field and the memories of his wisdom and candor will remain as an everlasting tribute. We will provide further information on how to honor his memory with his family once it becomes available. In lieu of flowers, the family prefers donations in honor of his memory to the charity of your choice.
Sincerely,
Sylvia
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Kevin Wu: Research Talk 11/8

Kevin Wu

Wednesday, November 8th, 2023

2:00 pm – 3:00 pm PST 

CCSR 0235

Zoom Link: https://stanford.zoom.us/j/95984592133?pwd=MDRXcyt4eTlCVlZhdHZYZ0prLzBGZz09

Title: Medical AI After Deployment: Data-driven analyses and methods for clinically viable AI

Abstract: Medical AI algorithms have undergone significant development and regulatory approval, with over 600 FDA-approved medical AI devices currently. However, their actual clinical safety and impact remain unclear. First, we analyze FDA submission documents and find that the majority of FDA approvals do not report multi-site evaluation, and nearly none have prospective analyses. Second, we track the occurrences of newly released AI billing codes in a nationwide insurance claims database and find that only a handful of products have meaningful clinical adoption. Finally, we systematically track device updating in FDA submissions and find that the majority of devices have not had updates to model weights since initial approval. Given these limitations, we propose several methods to address common issues with algorithmic deployment. First, we present a framework for understanding the marginal contribution of distribution shifts to overall model degradation. Second, we present a method for efficient missing data collection in the context of fixed models. Finally, we present ways to improve the robustness of evaluating medical LLMs.

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