Leveraging automated machine learning to benchmark, deconstruct, and compare frailty indices for predicting adverse spinal surgery outcomes.
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| Title: | Leveraging automated machine learning to benchmark, deconstruct, and compare frailty indices for predicting adverse spinal surgery outcomes. |
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| Authors: | Ghosh A; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Freda PJ; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Shahrestani S; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Orlenko A; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Scheer JK; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Obafemi-Ajayi T; Engineering Program, Missouri State University, Springfield, MO, USA., Moore JH; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. jason.moore@csmc.edu., Walker CT; Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA. corey.walker@cshs.org. |
| Source: | Scientific reports [Sci Rep] 2026 Jan 10; Vol. 16 (1), pp. 1849. Date of Electronic Publication: 2026 Jan 10. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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