Reliability and Clinical Utility of Machine Learning to Predict Stroke Prognosis: Comparison with Logistic Regression

참고문헌 (11)

1. Azur MJ, "Multiple imputation by chained equations: what is it and how does it work?", Int J Methods Psychiatr Res, 20, pp.40~49 (2011)
2. van Buuren S, "MICE : Multivariate imputation by chained equations in R", J Stat Softw, 45, pp.1~67 (2011)
3. "Consistent individualized feature attribution for tree ensembles"[URL]
4. Guo C, "On calibration of modern neural networks", Proceedings of the 34th International Conference on Machine Learning (ICML 2017), pp.2130~2143 (2017), Curran Associates
5. Niculescu-Mizil A, "Predicting good probabilities with supervised learning", Proceedings of the 29th International Conference on Machine Learning (ICML 2005), pp.625~632 (2005), ACM
6. Varoquaux G, "Scikit-learn : machine learning in python", GetMobile, 19, pp.29~33 (2015)
7. "Skorch documentation" (2017)[URL]
8. Naeini MP, "Obtaining well calibrated probabilities using bayesian binning", Proceedings of the AAAI 29th Conference on Artificial Intelligence, pp.2901~2907 (2015), AAAI Press
9. Moons KG, "Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD) : explanation and elaboration", Ann Intern Med, 162, pp.W1~W73 (2015)
10. Kerr KF, "Assessing the clinical impact of risk prediction models with decision curves : guidance for correct interpretation and appropriate use", J Clin Oncol, 34, pp.2534~2540 (2016)
11. Localio AR, "Beyond the usual prediction accuracy metrics : reporting results for clinical decision making", Ann Intern Med, 157, pp.294~295 (2012)

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