Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study.

ABSTRACT:

Introduction

Mortality and morbidity following surgery are pressing public health concerns in the USA. Traditional prediction models for postoperative adverse outcomes demonstrate good discrimination at the population level, but the ability to forecast an individual patient's trajectory in real time remains poor. We propose to apply machine learning techniques to perioperative time-series data to develop algorithms for predicting adverse perioperative outcomes.

Methods and analysis

This study will include all adult patients who had surgery at our tertiary care hospital over a 4-year period. Patient history, laboratory values, minute-by-minute intraoperative vital signs and medications administered will be extracted from the electronic medical record. Outcomes will include in

SUBMITTER: Fritz BA 

PROVIDER: S-EPMC5898287 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature