Preoperative Prediction of Postoperative Infections Using Machine Learning and Electronic Health Record Data.

Objective: To estimate preoperative risk of postoperative infections using structured electronic health record (EHR) data.

Background: Surveillance and reporting of postoperative infections is primarily done through costly, labor-intensive manual chart reviews on a small sample of patients. Automated methods using statistical models applied to postoperative EHR data have shown promise to augment manual review as they can cover all operations in a timely manner.

Effect of Present at Time of Surgery on Unadjusted and Risk-Adjusted Postoperative Complication Rate.

Background: Present at the time of surgery (PATOS) is an important measure to collect in postoperative complication surveillance systems because it may affect a patient's risk of a subsequent complication and the estimation of postoperative complication rates attributed to the healthcare system. The American College of Surgeons (ACS) NSQIP started collecting PATOS data for 8 postoperative complications in 2011, but no one has used these data to quantify how this may affect unadjusted and risk-adjusted postoperative complication rates.

Study Design: This study was a retrospective observational study of the ACS NSQIP database from 2012 to 2018.

Development and validation of models for detection of postoperative infections using structured electronic health records data and machine learning.

Background: Postoperative infections constitute more than half of all postoperative complications. Surveillance of these complications is primarily done through manual chart review, which is time consuming, expensive, and typically only covers 10% to 15% of all operations. Automated surveillance would permit the timely evaluation of and reporting of all operations.

Development and validation of a model for surveillance of postoperative bleeding complications using structured electronic health records data.

Background: Postoperative bleeding complications surveillance is done primarily through manual chart review. The purpose of this study was to develop and validate a detection model for postoperative bleeding complications using structured electronic health records data.

Methods: Patients who underwent operations at 1 of 5 hospitals within our local health system between 2013 and 2019 and whose complications were reported by the American College of Surgeons National Surgical Quality Improvement Program were included.

Comparing Statistical Tests for Differential Network Analysis of Gene Modules.

Genes often work together to perform complex biological processes, and "networks" provide a versatile framework for representing the interactions between multiple genes. Differential network analysis (DiNA) quantifies how this network structure differs between two or more groups/phenotypes (e.g.