Doudou Zhou
University of California, Davis
4 Papers
Doudou Zhou is an academic researcher from University of California, Davis. The author has contributed to research in topics: Nonparametric statistics & Parametric statistics. The author has an hindex of 2, co-authored 4 publications. Previous affiliations of Doudou Zhou include Harvard University.
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Papers
sureLDA: A multidisease automated phenotyping method for the electronic health record.
Yuri Ahuja,Doudou Zhou,Doudou Zhou,Zeling He,Jiehuan Sun,Jiehuan Sun,Victor M. Castro,Vivian S. Gainer,Shawn N. Murphy,Shawn N. Murphy,Chuan Hong,Tianxi Cai,Tianxi Cai +12 more
TL;DR: The sureLDA as mentioned in this paper algorithm combines attractive properties of PheNorm and LDA to achieve high accuracy and precision robust to diverse phenotype characteristics, which is well suited for large-scale electronic health record phenotyping for highly multiphenotype applications such as phenomewide association studies.
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Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data.
Chuan Hong,Chuan Hong,Everett Rush,Molei Liu,Doudou Zhou,Jiehuan Sun,Aaron Sonabend,Victor M. Castro,Petra Schubert,Vidul A. Panickan,Tianrun Cai,Lauren Costa,Zeling He,Nicholas Link,Ronald G. Hauser,J. Michael Gaziano,J. Michael Gaziano,J. Michael Gaziano,Shawn N. Murphy,George Ostrouchov,Yuk-Lam Ho,Edmon Begoli,Junwei Lu,Junwei Lu,Kelly Cho,Kelly Cho,Kelly Cho,Katherine P. Liao,Katherine P. Liao,Katherine P. Liao,Tianxi Cai,Tianxi Cai,VA Million Veteran Program +32 more
- 27 Oct 2021
TL;DR: In this article, a large-scale code embedding for a wide range of codified concepts from EHRs from two large medical centers was constructed and knowledge extraction via sparse embedding regression (KESER) was performed for feature selection and integrative network analysis.
Double/debiased machine learning for logistic partially linear model
Molei Liu,Yi Zhang,Doudou Zhou +2 more
TL;DR: Double/debiased machine learning approaches to infer the parametric component of a logistic partially linear model with the binary response following a conditional logistic model of a low dimensional linear parametric function of some key (exposure) covariates and a nonparametric function adjusting for the confounding effect of other covariates are proposed.
•Posted Content
Double/Debiased Machine Learning for Logistic Partially Linear Model
Molei Liu,Yi Zhang,Doudou Zhou +2 more
TL;DR: In this article, a double/debiased machine learning approach is proposed to estimate the parametric component of a logistic partially linear model with the binary response following a conditional logistic model of a low dimensional linear parametric function of some key (exposure) covariates and a nonparametric function adjusting for the confounding effect of other covariates.