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Erschienen in:

05.08.2022 | Correspondence

Developing the BreakThrough Pain Risk Score: an interpretable machine-learning-based risk score to predict breakthrough pain with labour epidural analgesia

verfasst von: Hon Sen Tan, MMed (Anaes), Nan Liu, PhD, Chin Wen Tan, PhD, Alex Tiong Heng Sia, MMed (Anaes), Ban Leong Sng, MMed (Anaes), FANZCA

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Excerpt

To the Editor, …

Tan HS, Liu N, Sultana R, et al. Prediction of breakthrough pain during labour neuraxial analgesia: comparison of machine learning and multivariable regression approaches. Int J Obstet Anesth 2021; 45: 99–110. https://doi.org/10.1016/j.ijoa.2020.08.010CrossRefPubMed

Khalilia M, Chakraborty S, Popescu M. Predicting disease risks from highly imbalanced data using random forest. BMC Med Inform Decis Mak 2011; 11: 51. https://doi.org/10.1186/1472-6947-11-51CrossRefPubMedPubMedCentral

Liu N, Koh ZX, Chua EC, et al. Risk scoring for prediction of acute cardiac complications from imbalanced clinical data. IEEE J Biomed Health Inform 2014; 18: 1894–902. https://doi.org/10.1109/jbhi.2014.2303481CrossRefPubMed

Xie F, Chakraborty B, Ong ME, Goldstein BA, Liu N. AutoScore: a machine learning-based automatic clinical score generator and its application to mortality prediction using electronic health records. JMIR Med Inform 2020; 8: e21798. https://doi.org/10.2196/21798CrossRefPubMedPubMedCentral

Tan HS, Sng BL, Sia AT. Reducing breakthrough pain during labour epidural analgesia: an update. Curr Opin Anaesthesiol 2019; 32: 307–14. https://doi.org/10.1097/aco.0000000000000713CrossRefPubMed

Titel: Developing the BreakThrough Pain Risk Score: an interpretable machine-learning-based risk score to predict breakthrough pain with labour epidural analgesia
verfasst von: Hon Sen Tan, MMed (Anaes)
Nan Liu, PhD
Chin Wen Tan, PhD
Alex Tiong Heng Sia, MMed (Anaes)
Ban Leong Sng, MMed (Anaes), FANZCA
Publikationsdatum: 05.08.2022
Verlag: Springer International Publishing
Erschienen in: Canadian Journal of Anesthesia/Journal canadien d'anesthésie / Ausgabe 10/2022
Print ISSN: 0832-610X
Elektronische ISSN: 1496-8975
DOI: https://doi.org/10.1007/s12630-022-02294-1

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