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Automated machine learning (AutoML) can predict 90-day mortality after gastrectomy for cancer. Sci Rep 2023 Jul 08;13(1):11051



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Pubmed Central ID




Scopus ID

2-s2.0-85164146540 (requires institutional sign-in at Scopus site)   2 Citations


Early postoperative mortality risk prediction is crucial for clinical management of gastric cancer. This study aims to predict 90-day mortality in gastric cancer patients undergoing gastrectomy using automated machine learning (AutoML), optimize models for preoperative prediction, and identify factors influential in prediction. National Cancer Database was used to identify stage I-III gastric cancer patients undergoing gastrectomy between 2004 and 2016. 26 features were used to train predictive models using AutoML. Performance on validation cohort was measured. In 39,108 patients, 90-day mortality rate was 8.8%. The highest performing model was an ensemble (AUC = 0.77); older age, nodal ratio, and length of inpatient stay (LOS) following surgery were most influential for prediction. Removing the latter two parameters decreased model performance (AUC 0.71). For optimizing models for preoperative use, models were developed to first predict node ratio or LOS, and these predicted values were inputted for 90-day mortality prediction (AUC of 0.73-0.74). AutoML performed well in predicting 90-day mortality in a larger cohort of gastric cancer patients that underwent gastrectomy. These models can be implemented preoperatively to inform prognostication and patient selection for surgery. Our study supports broader evaluation and application of AutoML to guide surgical oncologic care.

Author List

SenthilKumar G, Madhusudhana S, Flitcroft M, Sheriff S, Thalji S, Merrill J, Clarke CN, Maduekwe UN, Tsai S, Christians KK, Gamblin TC, Kothari AN


Callisia N. Clarke MD Chief, Associate Professor in the Surgery department at Medical College of Wisconsin
Anai N. Kothari MD Assistant Professor in the Surgery department at Medical College of Wisconsin
Ugwuji N. Maduekwe MD Associate Dean, Associate Professor in the Surgery department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Machine Learning
Retrospective Studies
Stomach Neoplasms