Predicting Postoperative Mortality With Deep Neural Networks and Natural Language Processing: Model Development and Validation.

ABSTRACT:

Background

Machine learning (ML) achieves better predictions of postoperative mortality than previous prediction tools. Free-text descriptions of the preoperative diagnosis and the planned procedure are available preoperatively. Because reading these descriptions helps anesthesiologists evaluate the risk of the surgery, we hypothesized that deep learning (DL) models with unstructured text could improve postoperative mortality prediction. However, it is challenging to extract meaningful concept embeddings from this unstructured clinical text.

Objective

This study aims to develop a fusion DL model containing structured and unstructured features to predict the in-hospital 30-day postoperative mortality before surgery. ML models for predicting postoperative mortality using preop

SUBMITTER: Chen PF 

PROVIDER: S-EPMC9131148 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature