title: Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures.
abstract: Electronic health records (EHR) provide opportunities to leverage vast arrays of data to help prevent adverse events, improve patient outcomes, and reduce hospital costs. This paper develops a postoperative complications prediction system by extracting data from the EHR and creating features. The analytic engine then provides model accuracy, calibration, feature ranking, and personalized feature responses. This allows clinicians to interpret the likelihood of an adverse event occurring, general causes for these events, and the contributing factors for each specific patient. The patient cohort considered was 5214 patients in Yale-New Haven Hospital undergoing major cardiovascular procedures. Cohort-specific models predicted the likelihood of postoperative respiratory failure and infection, and achieved an area under the receiver operating characteristic curve of 0.81 for respiratory failure and 0.83 for infection.
authors.forename: Bobak J
authors.initials: BJ
authors.lastname: Mortazavi
authors.forename: Nihar
authors.initials: N
authors.lastname: Desai
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.forename: Andreas
authors.initials: A
authors.lastname: Coppi
authors.forename: Fred
authors.initials: F
authors.lastname: Warner
authors.forename: Harlan M
authors.initials: HM
authors.lastname: Krumholz
authors.forename: Sahand
authors.initials: S
authors.lastname: Negahban
mesh_terms: D006348:Cardiac Surgical Procedures / Q000009:adverse effects*; D057286:Electronic Health Records; D006801:Humans; D000069550:Machine Learning; D015233:Models, Statistical; D011183:Postoperative Complications / Q000453:epidemiology*