Retrospective Cohort Study
Copyright ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Crit Care Med. Nov 19, 2019; 8(7): 120-126
Published online Nov 19, 2019. doi: 10.5492/wjccm.v8.i7.120
Machine learning in data abstraction: A computable phenotype for sepsis and septic shock diagnosis in the intensive care unit
Prabij Dhungana, Laura Piccolo Serafim, Arnaldo Lopez Ruiz, Danette Bruns, Timothy J Weister, Nathan Jerome Smischney, Rahul Kashyap
Prabij Dhungana, Nathan Jerome Smischney, Rahul Kashyap, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55905, United States
Prabij Dhungana, Laura Piccolo Serafim, Arnaldo Lopez Ruiz, Nathan Jerome Smischney, Rahul Kashyap, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN 55905, United States
Laura Piccolo Serafim, Arnaldo Lopez Ruiz, Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, United States
Danette Bruns, Timothy J Weister, Anesthesia Clinical Research Unit, Mayo Clinic, MN 55905, United States
Author contributions: All listed authors provided intellectual contribution and made critical revisions of this paper; Kashyap R, Lopes Ruiz A and Smischney NJ contributed to study conception and design; Dhungana P, Piccolo Serafim L, BrunsD and Weister TJ contributed to data acquisition; Dhungana P, Piccolo Serafim L, Smischney NJ and Kashyap R contributed to data analysis; all authors approved the final version of the manuscript.
Institutional review board statement: The study was reviewed and approved by the Mayo Clinic Institutional Review Board.
Informed consent statement: Retrospective study was exempt from need for informed consent.
Conflict-of-interest statement: Authors declare no conflict of interests for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Rahul Kashyap, MBBS, Assistant Professor, MBA, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. kashyap.rahul@mayo.edu
Telephone: +1-507-2557196
Received: April 23, 2019
Peer-review started: May 8, 2019
First decision: August 2, 2019
Revised: August 21, 2019
Accepted: October 27, 2019
Article in press: October 27, 2019
Published online: November 19, 2019
Processing time: 212 Days and 21.9 Hours
Core Tip

Core tip: This study presents and validates a supervised machine learning model for the identification of sepsis and septic shock cases using electronic medical records as an alternative to manual chart review. This method showed to be an efficient, fast and reliable option for retrospective data abstraction, with the potential to be applied to other clinical conditions.