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Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.

J. Lemmon, L. Guo, E. Steinberg, K. Morse, S. Fleming, C. Aftandilian, S. Pfohl, J. Posada, N. Shah, J. Fries, and L. Sung. J. Am. Medical Informatics Assoc., 30 (12): 2004-2011 (November 2023)

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Keith E Bonn

Keith E Karlsson

Jeremy Morse

Hans Morsek

Christiane Mörsel

Other publications of authors with the same name

Standing on FURM ground - A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems.A. Callahan, D. McElfresh, J. Banda, G. Bunney, D. Char, J. Chen, C. Corbin, D. Dash, N. Downing, S. Jain and 14 other author(s). CoRR, (2024)MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records.S. Fleming, A. Lozano, W. Haberkorn, J. Jindal, E. Reis, R. Thapa, L. Blankemeier, J. Genkins, E. Steinberg, A. Nayak and 20 other author(s). AAAI, page 22021-22030. AAAI Press, (2024)MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records.S. Fleming, A. Lozano, W. Haberkorn, J. Jindal, E. Reis, R. Thapa, L. Blankemeier, J. Genkins, E. Steinberg, A. Nayak and 20 other author(s). CoRR, (2023)Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.J. Lemmon, L. Guo, E. Steinberg, K. Morse, S. Fleming, C. Aftandilian, S. Pfohl, J. Posada, N. Shah, J. Fries and 1 other author(s). J. Am. Medical Informatics Assoc., 30 (12): 2004-2011 (November 2023)A Natural Language Processing Model to Identify Confidential Content in Adolescent Clinical Notes.N. Rabbani, M. Bedgood, C. Brown, E. Steinberg, R. Goldstein, J. Carlson, N. Pageler, and K. Morse. Appl. Clin. Inform., 14 (3): 400-407 (May 2023)A survey of extant organizational and computational setups for deploying predictive models in health systems.S. Kashyap, K. Morse, B. Patel, and N. Shah. J. Am. Medical Informatics Assoc., 28 (11): 2445-2450 (2021)A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records.L. Guo, J. Fries, E. Steinberg, S. Fleming, K. Morse, C. Aftandilian, J. Posada, N. Shah, and L. Sung. CoRR, (2023)Learning competing risks across multiple hospitals: one-shot distributed algorithms.D. Zhang, J. Tong, N. Jing, Y. Yang, C. Luo, Y. Lu, D. Christakis, D. Güthe, M. Hornig, K. Kelleher and 6 other author(s). J. Am. Medical Informatics Assoc., 31 (5): 1102-1112 (2024)Instability in clinical risk stratification models using deep learning.D. Martinez, A. Yakubovich, M. Seneviratne, Á. Lelkes, A. Tyagi, J. Kemp, E. Steinberg, N. Downing, R. Li, K. Morse and 2 other author(s). ML4H@NeurIPS, volume 193 of Proceedings of Machine Learning Research, page 552-565. PMLR, (2022)The Prevalence of Confidential Content in Adolescent Progress Notes Prior to the 21st Century Cures Act Information Blocking Mandate.M. Bedgood, N. Rabbani, C. Brown, R. Goldstein, J. Carlson, E. Steinberg, A. Powell, N. Pageler, and K. Morse. Appl. Clin. Inform., 14 (2): 337-344 (March 2023)

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Disambiguation of "Morse, Keith E."

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Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.

Please choose a person to relate this publication to

Keith E Bonn

Keith E Karlsson

Jeremy Morse

Hans Morsek

Christiane Mörsel

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Morse, Keith E."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.

Please choose a person to relate this publication to

Keith E Bonn

Keith E Karlsson

Jeremy Morse

Hans Morsek

Christiane Mörsel

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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history of this post
URL
DOI
BibTeX
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APA
Chicago
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Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.