Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery

Hung Kuan Yen; Jiun Jen Yang; Olivier Q. Groot; Mao Hsu Yen; Ming Hsiao Hu

doi:10.1016/j.spinee.2022.10.011

Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery

Hung Kuan Yen
, Jiun Jen Yang
, Olivier Q. Groot
, Mao Hsu Yen
, Ming Hsiao Hu^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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Original language	English
Pages (from-to)	312-314
Number of pages	3
Journal	Spine Journal
Volume	23
Issue number	2
DOIs	https://doi.org/10.1016/j.spinee.2022.10.011
Publication status	Published - Feb 2023

Keywords

Artificial intelligence
Machine learning
Random splitting
Spinal metastasis
Temporal splitting

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10.1016/j.spinee.2022.10.011

1-s2.0-S1529943022009937-mainFinal published version, 481 KBLicence: Taverne

Cite this

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title = "Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery",

keywords = "Artificial intelligence, Machine learning, Random splitting, Spinal metastasis, Temporal splitting",

author = "Yen, \{Hung Kuan\} and Yang, \{Jiun Jen\} and Groot, \{Olivier Q.\} and Yen, \{Mao Hsu\} and Hu, \{Ming Hsiao\}",

year = "2023",

month = feb,

doi = "10.1016/j.spinee.2022.10.011",

language = "English",

volume = "23",

pages = "312--314",

journal = "Spine Journal",

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AU - Yen, Hung Kuan

AU - Yang, Jiun Jen

AU - Groot, Olivier Q.

AU - Yen, Mao Hsu

AU - Hu, Ming Hsiao

PY - 2023/2

Y1 - 2023/2

KW - Artificial intelligence

KW - Machine learning

KW - Random splitting

KW - Spinal metastasis

KW - Temporal splitting

UR - https://www.scopus.com/pages/publications/85142734233

U2 - 10.1016/j.spinee.2022.10.011

DO - 10.1016/j.spinee.2022.10.011

M3 - Article

C2 - 36336254

AN - SCOPUS:85142734233

SN - 1529-9430

VL - 23

SP - 312

EP - 314

JO - Spine Journal

JF - Spine Journal

IS - 2

ER -

Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery

Keywords

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