Complication Prediction after Esophagectomy with Machine Learning

Jorn-Jan van de Beld*, David Crull, Julia Mikhal, Jeroen Geerdink, Anouk Veldhuis, Mannes Poel, Ewout A. Kouwenhoven

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Esophageal cancer can be treated effectively with esophagectomy; however, the postoperative complication rate is high. In this paper, we study to what extent machine learning methods can predict anastomotic leakage and pneumonia up to two days in advance. We use a dataset with 417 patients who underwent esophagectomy between 2011 and 2021. The dataset contains multimodal temporal information, specifically, laboratory results, vital signs, thorax images, and preoperative patient characteristics. The best models scored mean test set AUROCs of 0.87 and 0.82 for leakage 1 and 2 days ahead, respectively. For pneumonia, this was 0.74 and 0.61 for 1 and 2 days ahead, respectively. We conclude that machine learning models can effectively predict anastomotic leakage and pneumonia after esophagectomy.
Original languageEnglish
Article number439
JournalDiagnostics
Volume14
Issue number4
DOIs
Publication statusPublished - 17 Feb 2024

Keywords

  • Esophageal cancer
  • Clinical Decision Support
  • Multimodal Machine Learning
  • Temporal modelling

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