Document Type
Article
Publication Title
Studies in health technology and informatics
PubMed ID
34042730
MeSH Headings (Medical Subject Headings)
Anesthesia; Colonoscopy; Conscious Sedation; Humans; Machine Learning
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.
First Page
183
Last Page
187
DOI
10.3233/SHTI210145
Publication Date
5-27-2021
Recommended Citation
Syed, Shorabuddin; Syed, Mahanazuddin; Prior, Fred; Zozus, Meredith; Syeda, Hafsa Bareen; Greer, Melody L.; Bhattacharyya, Sudeepa; and Garg, Shashank, "Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures" (2021). Arkansas Biosciences Institute. 48.
https://arch.astate.edu/abi/48
