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Xu Shi
Researcher at University of Michigan
Publications - 81
Citations - 1344
Xu Shi is an academic researcher from University of Michigan. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 13, co-authored 39 publications receiving 540 citations. Previous affiliations of Xu Shi include University of Washington & Harvard University.
Papers
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Journal ArticleDOI
Global Prevalence of Post COVID-19 Condition or Long COVID: A Meta-Analysis and Systematic Review
TL;DR: This study finds post CO VID-19 condition prevalence is substantial; the health effects of COVID-19 appear to be prolonged and can exert stress on the healthcare system.
Journal ArticleDOI
Association of early imaging for back pain with clinical outcomes in older adults
Jeffrey G. Jarvik,Laura S. Gold,Bryan A. Comstock,Patrick J. Heagerty,Sean D. Rundell,Judith A. Turner,Andrew L. Avins,Zoya Bauer,Brian W. Bresnahan,Janna L. Friedly,Kathryn T. James,Larry Kessler,Srdjan S. Nedeljkovic,David R. Nerenz,Xu Shi,Sean D. Sullivan,Leighton Chan,Jason M. Schwalb,Richard A. Deyo +18 more
TL;DR: Among older adults with a new primary care visit for back pain, early imaging was not associated with better 1-year outcomes, and the value of early diagnostic imaging in older adults forBack pain without radiculopathy is uncertain.
Proceedings ArticleDOI
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Andrew L. Beam,Benjamin Kompa,Allen Schmaltz,Inbar Fried,Griffin M. Weber,Nathan Palmer,Xu Shi,Tianxi Cai,Isaac S. Kohane +8 more
TL;DR: This article demonstrates how an insurance claims database of 60 million members, a collection of clinical notes, and 1.7 million full text biomedical journal articles can be combined to embed concepts into a common space, resulting in the largest ever set of embeddings for 108,477 medical concepts.
Posted Content
An Introduction to Proximal Causal Learning
TL;DR: A formal potential outcome framework for proximal causal learning is introduced, which while explicitly acknowledging covariate measurements as imperfect proxies of confounding mechanisms, offers an opportunity to learn about causal effects in settings where exchangeability on the basis of measured covariates fails.
Journal ArticleDOI
Multiply robust causal inference with double‐negative control adjustment for categorical unmeasured confounding
TL;DR: This work establishes non-parametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables, and provides a general semiparametric framework for obtaining inferences about the AtE while leveraging information about a possibly large number of measured covariates.