An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes.

ABSTRACT:

Introduction

Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been shown to decrease the associated mortality and morbidity. Combining deep neural networks and genomics with the already established clinical predictors may hold promise for improvement.

Methods

The UK Biobank was utilized to build linear and deep learning models for the prediction of surgery relevant outcomes. An initial GWAS for the relevant outcomes was initially conducted to select the Single Nucleotide Polymorphisms for inclusion in the models. Model performance was assessed with Receiver Operator Characteristics of the Area Un

SUBMITTER: Christensen MA 

PROVIDER: S-EPMC11249253 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature