Why the study?
Achieving complete surgical cytoreduction in advanced stage high grade serous ovarian cancer requires CCU beds, and machine learning could improve the accuracy of predicting CCU admissions.
Do Machine Learning algorithms improve the accuracy of predicting CCU admission in advanced stage high grade serous ovarian cancer patients undergoing cytoreductive surgery compared to conventional logistic regression?
Population
291 advanced stage high grade serous ovarian cancer patients undergoing cytoreductive surgery
Comparison
Machine learning algorithms vs conventional logistic regression
Design
Cohort study
Authors
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ML models may aid CCU planning for advanced HGSOC surgery; leaves open need for prospective validation before adoption.
Do Machine Learning algorithms improve the accuracy of predicting CCU admission in advanced stage high grade serous ovarian cancer patients undergoing cytoreductive surgery compared to conventional logistic regression?
Machine learning algorithms, specifically linear and quadratic discriminant methods, provide highly accurate prediction of CCU admission for ovarian cancer patients undergoing cytoreductive surgery.
Laios et al. (2021) studied this question.
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