Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures

Shorabuddin Syed, Mahanazuddin Syed, Fred Prior, Meredith Zozus, Hafsa Bareen Syeda, Melody L. Greer, Sudeepa Bhattacharyya, Shashank Garg

Research output: Contribution to journalArticlepeer-review

Abstract

Endoscopy procedures are often performed with either moderate or deep sedation. While deep sedation is costly, procedures with moderate sedation are not always well tolerated resulting in patient discomfort, and are often aborted. Due to lack of clear guidelines, the decision to utilize moderate sedation or anesthesia for a procedure is made by the providers, leading to high variability in clinical practice. The objective of this study was to build a Machine Learning (ML) model that predicts if a colonoscopy can be successfully completed with moderate sedation based on patients' demographics, comorbidities, and prescribed medications. XGBoost model was trained and tested on 10,025 colonoscopies (70% - 30%) performed at University of Arkansas for Medical Sciences (UAMS). XGBoost achieved average area under receiver operating characteristic curve (AUC) of 0.762, F1-score to predict procedures that need moderate sedation was 0.85, and precision and recall were 0.81 and 0.89 respectively. The proposed model can be employed as a decision support tool for physicians to bolster their confidence while choosing between moderate sedation and anesthesia for a colonoscopy procedure.

Original languageEnglish (US)
Pages (from-to)183-187
Number of pages5
JournalStudies in Health Technology and Informatics
Volume281
DOIs
StatePublished - May 27 2021

Keywords

  • Colonoscopy
  • electronic health records
  • machine learning
  • moderate sedation
  • quality improvement

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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