title: Accelerating Chart Review Using Automated Methods on Electronic Health Record Data for Postoperative Complications.
abstract: Manual Chart Review (MCR) is an important but labor-intensive task for clinical research and quality improvement. In this study, aiming to accelerate the process of extracting postoperative outcomes from medical charts, we developed an automated postoperative complications detection application by using structured electronic health record (EHR) data. We applied several machine learning methods to the detection of commonly occurring complications, including three subtypes of surgical site infection, pneumonia, urinary tract infection, sepsis, and septic shock. Particularly, we applied one single-task and five multi-task learning methods and compared their detection performance. The models demonstrated high detection performance, which ensures the feasibility of accelerating MCR. Specifically, one of the multi-task learning methods, propensity weighted observations (PWO) demonstrated the highest detection performance, with single-task learning being a close second.
authors.affiliation: Institute for Health Informatics.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Hu
authors.affiliation: Institute for Health Informatics; Department of Surgery.
authors.forename: Genevieve B
authors.initials: GB
authors.lastname: Melton
authors.affiliation: Department of Computer Science and Engineering.
authors.forename: Nathan D
authors.initials: ND
authors.lastname: Moeller
authors.affiliation: Institute for Health Informatics; Department of Surgery.
authors.forename: Elliot G
authors.initials: EG
authors.lastname: Arsoniadis
authors.affiliation: Institute for Health Informatics.
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Surgery.
authors.forename: Mary R
authors.initials: MR
authors.lastname: Kwaan
authors.affiliation: Department of Surgery.
authors.forename: Eric H
authors.initials: EH
authors.lastname: Jensen
authors.affiliation: Institute for Health Informatics; Department of Medicine, University of Minnesota, MN.
authors.forename: Gyorgy J
authors.initials: GJ
authors.lastname: Simon
mesh_terms: D000465:Algorithms; D057286:Electronic Health Records; D006801:Humans; D000069550:Machine Learning; D008485:Medical Audit / Q000379:methods*; D011014:Pneumonia / Q000175:diagnosis; D011183:Postoperative Complications / Q000175:diagnosis*; D018805:Sepsis / Q000175:diagnosis; D012772:Shock, Septic / Q000175:diagnosis; D013530:Surgical Wound Infection / Q000175:diagnosis; D014552:Urinary Tract Infections / Q000175:diagnosis