Article Figures & Data
Figures
- Figure 1
Gartner’s hype cycle. Source: Reprinted with permission from Oosterhoff JHF, Doornberg JN. Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner’s hype cycle. EFORT Open Rev. 2020;5(10):593–603. © 2020 Oosterhoff and Doornerg.
- Figure 2
The first 3 stages in model development: preparation, development, and internal validation. TRIPOD, transparent reporting of a multivariable prediction model for individual prognosis or diagnosis; PROBAST, prediction model risk of bias assessment tool; EHR, electronic health record.
- Figure 3
The last 3 stages in model development: presentation, external validation, and implementation. TRIPOD, transparent reporting of a multivariable prediction model for individual prognosis or diagnosis; PROBAST, prediction model risk of bias assessment tool; EHR, electronic health record.
- Figure 4
Overview of validation measures. C-statistic, concordance statistic; AUC, area under the curve; ROC, receiver operating characteristic.
- Figure 5
Model specification. With a random forest algorithm, we created many different predictor sets (sets with different input variables) which we tested with 10-fold cross-validation to find the optimal set of predictors. This technique fits the model 10 times, with each fit being performed on a training set of a different 90% of the data with the remaining 10% as a holdout set for validation. Each fit produces a performance metric, and the average of all these fits results in the average performance of a predictor set.
- Figure 6
Model estimation. For SORG-MLA, we used 5 different models: random forests, stochastic gradient boosting, neural network, support vector machine, and penalized logistic regression. SORG-MLA, the Skeletal Oncology Research Group machine learning algorithms.
- Figure 7
Calibration: calibration plot of SORG-MLA predicting 90-d and 1-y mortality at (A) internal validation and (B) external validation (Taiwan). Comparing these plots demonstrates that SORG-MLA performs differently in other populations, highlighting the importance of external validation. SORG-MLA, the Skeletal Oncology Research Group machine learning algorithms. Source: Reprinted from with permission from The Spine Journal, Vol 21, Yang J-J, Chen C-W, Fourman MS, et al, International external validation of the SORG machine learning algorithms for predicting 90-day and one-year survival of patients with spine metastases using a Taiwanese cohort, 1670-1678, Copyright 2021, with permission from Elsevier.56
- Figure 8
Decision curve analysis: decision curve of SORG-MLA predicting 90-d and 1-y mortality at external validation. SORG-MLA, the Skeletal Oncology Research Group machine learning algorithms. Source: Reprinted from The Spine Journal, Vol 21, Shah AA, Karhade AV, Park HY, et al, Updated external validation of the SORG machine learning algorithms for prediction of ninety-day and one-year mortality after surgery for spinal metastasis, 1679–1686, Copyright 2021, with permission from Elsevier.57
Tables
Study Institution, City, Country Patients Calibration: Intercept Calibration: Slope Discrimination Brier Score Model Brier Score Null Decision Curve Analysis Performed Karhade et al, 202054 John Hopkins University, School of Medicine, Baltimore, USA 176 90-d Mortality −0.10 0.64 0.75 0.157 0.176 Yes 1-y Mortality 0.43 0.77 0.77 0.199 0.246 Yes Bongers et al, 202055 Memorial Sloan-Kettering Cancer Center, New York, USA 200 90-d Mortality −0.07 0.64 0.81 0.17 0.20 Yes 1-y Mortality 0.57 0.85 0.84 0.16 0.23 Yes Yang et al, 202156 National Taiwan University Hospital, Taipei, Taiwan 427 90-d Mortality 0.81 0.51 0.73 0.17 0.19 Yes 1-y Mortality 0.08 0.59 0.74 0.20 0.24 Yes Shah et al, 202157 David Geffen School of Medicine at UCLA, USA 298 90-d Mortality −0.65 0.80 0.84 0.13 0.17 Yes 1-y Mortality 0.08 1.20 0.90 0.13 0.25 Yes Abbreviation: UCLA, University of California, Los Angeles.
Figure S1.
Figure S2.
In this issue
![[8500supp002.jpg]](http://www.ijssurgery.com/content/ijss/17/S1/S45/DC2/embed/inline-supplementary-material-2.jpg?download=true){kind=link}
Jump to section
Related Articles
Cited By...
- No citing articles found.