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Adjusting for indirectly measured confounding using large-scale propensity score.

L. Zhang, Y. Wang, M. Schuemie, D. Blei, and G. Hripcsak. J. Biomed. Informatics, (2022)

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Zhang Zhang

Meng Zhang

Methods and implementations of road-network matchingM. Zhang. TU München, (2009)

Shuying Zhang

Gang Zhang

Yan Zhang

Other publications of authors with the same name

The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records.L. Zhang, Y. Wang, A. Ostropolets, J. Mulgrave, D. Blei, and G. Hripcsak. MLHC, volume 106 of Proceedings of Machine Learning Research, page 490-512. PMLR, (2019)Semi-Supervised Transfer Learning Framework for Aging-Aware Library Characterization.Z. Tang, C. Li, H. You, Z. Di, L. Zhang, X. Liu, Y. Wang, Y. Dai, and G. Bai. IEEE Trans. Circuits Syst. II Express Briefs, 71 (3): 1156-1160 (March 2024)A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making.L. Zhang, L. Richter, Y. Wang, A. Ostropolets, N. Elhadad, D. Blei, and G. Hripcsak. CoRR, (2022)Evaluation of Large-scale Propensity Score Modeling and Covariate Balance on Potential Unmeasured Confounding in Observational Research.R. Chen, M. Schuemie, M. Suchard, A. Ostropolets, L. Zhang, P. Ryan, and G. Hripcsak. AMIA, AMIA, (2020)Using Data Assimilation to Predict Post-Operative Bariatric Surgery Glycemic Status in Adolescents.L. Richter, B. Albert, L. Zhang, I. Fennoy, D. Albers, and G. Hripcsak. AMIA, AMIA, (2022)Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study.A. Ostropolets, Y. Albogami, M. Conover, J. Banda, W. Jr., C. Blacketer, P. Desai, S. DuVall, S. Fortin, J. Gilbert and 28 other author(s). J. Am. Medical Informatics Assoc., 30 (5): 859-868 (April 2023)A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time.A. Ostropolets, L. Zhang, and G. Hripcsak. J. Am. Medical Informatics Assoc., 27 (12): 1968-1976 (2020)Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium.H. Jeong, S. Jabbour, Y. Yang, R. Thapa, H. Mozannar, W. Han, N. Mehandru, M. Wornow, V. Lialin, X. Liu and 33 other author(s). CoRR, (2024)The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs).L. Zhang, Y. Wang, A. Ostropolets, J. Mulgrave, D. Blei, and G. Hripcsak. CoRR, (2019)Predicting pressure injury using nursing assessment phenotypes and machine learning methods.W. Song, M. Kang, L. Zhang, W. Jung, J. Song, D. Bates, and P. Dykes. J. Am. Medical Informatics Assoc., 28 (4): 759-765 (2021)

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Disambiguation of "Zhang, Linying"

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Adjusting for indirectly measured confounding using large-scale propensity score.

Please choose a person to relate this publication to

Zhang Zhang

Meng Zhang

Shuying Zhang

Gang Zhang

Yan Zhang

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Zhang, Linying"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Adjusting for indirectly measured confounding using large-scale propensity score.

Please choose a person to relate this publication to

Zhang Zhang

Meng Zhang

Shuying Zhang

Gang Zhang

Yan Zhang

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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Adjusting for indirectly measured confounding using large-scale propensity score.