Development and validation of 'Patient Optimizer' (POP) algorithms for predicting surgical risk with machine learning.

ABSTRACT:

Background

Pre-operative risk assessment can help clinicians prepare patients for surgery, reducing the risk of perioperative complications, length of hospital stay, readmission and mortality. Further, it can facilitate collaborative decision-making and operational planning.

Objective

To develop effective pre-operative risk assessment algorithms (referred to as Patient Optimizer or POP) using Machine Learning (ML) that predict the development of post-operative complications and provide pilot data to inform the design of a larger prospective study.

Methods

After institutional ethics approval, we developed a base model that encapsulates the standard manual approach of combining patient-risk and procedure-risk. In an automated process, additional variables were included

SUBMITTER: Kowadlo G 

PROVIDER: S-EPMC10929113 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature