Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study

Autor: Betina Ristorp Andersen, Ida Ammitzbøll, Jesper Hinrich, Sune Lehmann, Charlotte Vibeke Ringsted, Ellen Christine Leth Løkkegaard, Martin G Tolsgaard

Publikováno v: Andersen, B R, Ammitzboll, I, Hinrich, J, Lehmann, S, Ringsted, C V, Lokkegaard, E C L & Tolsgaard, M G 2022, ' Using machine learning to identify quality-of-care predictors for emergency caesarean sections : a retrospective cohort study ', BMJ Open, vol. 12, no. 3, 049046 . https://doi.org/10.1136/bmjopen-2021-049046
Andersen, B R, Ammitzbøll, I, Hinrich, J, Lehmann, S, Ringsted, C V, Løkkegaard, E C L & Tolsgaard, M G 2022, ' Using machine learning to identify quality-of-care predictors for emergency caesarean sections : A retrospective cohort study ', BMJ Open, vol. 12, no. 3, e049046 . https://doi.org/10.1136/bmjopen-2021-049046
Andersen, B R, Ammitzbøll, I, Hinrich, J, Lehmann, S, Ringsted, C V, Løkkegaard, E C L & Tolsgaard, M G 2022, ' Using machine learning to identify quality-of-care predictors for emergency caesarean sections : a retrospective cohort study ', BMJ Open, vol. 12, no. 3, e049046 . https://doi.org/10.1136/bmjopen-2021-049046

ObjectivesEmergency caesarean sections (ECS) are time-sensitive procedures. Multiple factors may affect team efficiency but their relative importance remains unknown. This study aimed to identify the most important predictors contributing to quality

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https://pubmed.ncbi.nlm.nih.gov/35256439