Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy

Purpose The purpose of this study was to compare the effectiveness of ensemble methods (e.g., random forests) and single‐model methods (e.g., logistic regression and decision trees) in predictive modeling of post‐RT treatment failure and adverse events (AEs) for breast cancer patients using automati...

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Veröffentlicht in:	Medical physics (Lancaster) 2019-02, Vol.46 (2), p.1054-1063
Hauptverfasser:	Lindsay, William D., Ahern, Christopher A., Tobias, Jacob S., Berlind, Christopher G., Chinniah, Chidambaram, Gabriel, Peter E., Gee, James C., Simone, Charles B.
Format:	Artikel
Sprache:	eng
Schlagworte:	automated data extraction ensemble methods machine learning predictive modeling radiotherapy outcomes
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