University of Twente Student Theses


Predicting postoperative complications after esophagectomy using machine learning algorithms

Reincke, M.C. (2021) Predicting postoperative complications after esophagectomy using machine learning algorithms.

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Abstract:Background: Currently, surgical resection of the esophagus is the only curative treatment for a patient with non-metastatic esophagus cancer. Despite a substantial improvement in the survival of these patients, esophagectomy is burdened with high procedure-related morbidity. The most common and severe postoperative complications after esophagectomy are pneumonia and anastomotic leakage. To assist clinicians in the early detection of postoperative complications, machine learning models could support in detecting novel predictors and patterns of postoperative deterioration. Objective: This research aimed to explore the ability of machine learning algorithms to predict major complications in patients who underwent esophagectomy by using structured and unstructured postoperative data. Methods: Postoperative structured and unstructured data of patients who underwent esophageal resection for cancer were extracted from the electronic health record. These patients were divided into two groups, one reference group, group 0 and a group with patients who suffered from either pneumonia or anastomotic leakage, group 1. The structured postoperative data contained vital signs and laboratory tests. The unstructured data consisted of nursing assessments reports, which we converted to text features by using a bag of words model. Both the structured and unstructured data was used to predict postoperative complications, specifically anastomotic leakage and pneumonia, using logistic regression, support vector machines, decision trees and random forest. Results: We identified 164 patients of which 112 patients belong to group 0 and the other 52 in group 1. When using structured data alone we predicted postoperative complications using random forest with an area under the receiver operating curve of 0.88, a sensitivity of 44% and a specificity of 94%. After the addition of text features, the AUC improved to 0.90 and the specificity increased to 97%, while the sensitivity decreased to 12%. The overall performance of all of our models did not improve when adding text features to the models. Conclusions: This study revealed that machine learning models have an overall fair prediction of postoperative complications after surgery when using postoperative data, both structured and unstructured. Within these models, C-reactive protein and temperature are important predictors of anastomotic leakage and pneumonia. Furthermore, the potential of text features needs to be further explored to improve the prediction of postoperative complications after esophagectomy.
Item Type:Essay (Master)
ZGT, Almelo, Nederland
Faculty:TNW: Science and Technology
Subject:44 medicine
Programme:Technical Medicine MSc (60033)
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