Corrigendum to ‘Machine learning prediction of hospital patient need for post-acute care using an admission mobility measure is robust across patient diagnoses’ [Health Policy and Technology 12 (2023) 100,754] (Health Policy and Technology (2023) 12(2), (S2211883723000308), (10.1016/j.hlpt.2023.100754))

Daniel L. Young; Rebecca Engels; Elizabeth Colantuoni; Lisa Aronson Friedman; Erik H. Hoyer

doi:10.1016/j.hlpt.2023.100825

Corrigendum to ‘Machine learning prediction of hospital patient need for post-acute care using an admission mobility measure is robust across patient diagnoses’ [Health Policy and Technology 12 (2023) 100,754] (Health Policy and Technology (2023) 12(2), (S2211883723000308), (10.1016/j.hlpt.2023.100754))

Daniel L. Young, Rebecca Engels, Elizabeth Colantuoni, Lisa Aronson Friedman, Erik H. Hoyer

Research output: Contribution to journal › Comment/debate › peer-review

Original language	English (US)
Article number	100825
Journal	Health Policy and Technology
Volume	12
Issue number	4
DOIs	https://doi.org/10.1016/j.hlpt.2023.100825
State	Published - Dec 2023

ASJC Scopus subject areas

Biomedical Engineering
Health Policy

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10.1016/j.hlpt.2023.100825

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Young, D. L., Engels, R., Colantuoni, E., Friedman, L. A., & Hoyer, E. H. (2023). Corrigendum to ‘Machine learning prediction of hospital patient need for post-acute care using an admission mobility measure is robust across patient diagnoses’ [Health Policy and Technology 12 (2023) 100,754] (Health Policy and Technology (2023) 12(2), (S2211883723000308), (10.1016/j.hlpt.2023.100754)). Health Policy and Technology, 12(4), Article 100825. https://doi.org/10.1016/j.hlpt.2023.100825

Corrigendum to ‘Machine learning prediction of hospital patient need for post-acute care using an admission mobility measure is robust across patient diagnoses’ [Health Policy and Technology 12 (2023) 100,754] (Health Policy and Technology (2023) 12(2), (S2211883723000308), (10.1016/j.hlpt.2023.100754)). / Young, Daniel L.; Engels, Rebecca; Colantuoni, Elizabeth et al.
In: Health Policy and Technology, Vol. 12, No. 4, 100825, 12.2023.

Research output: Contribution to journal › Comment/debate › peer-review

Young, DL, Engels, R, Colantuoni, E, Friedman, LA & Hoyer, EH 2023, 'Corrigendum to ‘Machine learning prediction of hospital patient need for post-acute care using an admission mobility measure is robust across patient diagnoses’ [Health Policy and Technology 12 (2023) 100,754] (Health Policy and Technology (2023) 12(2), (S2211883723000308), (10.1016/j.hlpt.2023.100754))', Health Policy and Technology, vol. 12, no. 4, 100825. https://doi.org/10.1016/j.hlpt.2023.100825

Young DL, Engels R, Colantuoni E, Friedman LA, Hoyer EH. Corrigendum to ‘Machine learning prediction of hospital patient need for post-acute care using an admission mobility measure is robust across patient diagnoses’ [Health Policy and Technology 12 (2023) 100,754] (Health Policy and Technology (2023) 12(2), (S2211883723000308), (10.1016/j.hlpt.2023.100754)). Health Policy and Technology. 2023 Dec;12(4):100825. doi: 10.1016/j.hlpt.2023.100825

Young, Daniel L. ; Engels, Rebecca ; Colantuoni, Elizabeth et al. / Corrigendum to ‘Machine learning prediction of hospital patient need for post-acute care using an admission mobility measure is robust across patient diagnoses’ [Health Policy and Technology 12 (2023) 100,754] (Health Policy and Technology (2023) 12(2), (S2211883723000308), (10.1016/j.hlpt.2023.100754)). In: Health Policy and Technology. 2023 ; Vol. 12, No. 4.

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ASJC Scopus subject areas

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