Corrigendum: “Predicting respiratory failure after pulmonary lobectomy using machine learning techniques” (Surgery (2020) 168(4) (743–752), (S0039606020303317), (10.1016/j.surg.2020.05.032))

Siavash Bolourani, Ping Wang, Vihas M. Patel, Frank Manetta, Paul C. Lee

Research output: Contribution to journalComment/debate

1 Scopus citations

Abstract

We discovered an error in a line of the code used in the experimentation of the 2 methods presented in Fig 3.1 The error is in the section where the prediction models are being performed and introduces a “leak” between the training set and testing set. This error exists for both models presented. The implication of this error is that the predictive ability of the 2 models is overestimated. After correcting the error, the area under the receiver operating characteristic of the models diminishes by ~10%. The sensitivity and specificity of the model presented in Fig 3, A (random forest alone) are 40.1% and 96.6%, respectively. The sensitivity and specificity of the model in Fig 3, B (random forest +Combined over and under-sampling using SMOTE and Edited Nearest Neighbours [SMOTEENN] algorithm) are 69.4% and 85.0%, respectively. The discussion regarding Fig 3 remains the same: the random forest model was the accurate model; after applying the SMOTEENN method, the sensitivity of the model increases, which comes at the cost of decreasing specificity. The oversampling SMOTEENN model used is from Python library imblearn.combine.SMOTEENN. This error does not affect the methodology, the rest of the results, the discussion, or the conclusion of this article. For the reference, we provide the corrected version of the model presented in Fig 3. We would like to convey our sincere apologies to the readers and editors for the inconvenience this has caused.

Original languageEnglish
Pages (from-to)1001
Number of pages1
JournalSurgery
Volume169
Issue number4
DOIs
StatePublished - Apr 2021
Externally publishedYes

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