Prediction of lactate concentrations after cardiac surgery using machine learning and deep learning approaches.

ABSTRACT:

Background

Although conventional prediction models for surgical patients often ignore intraoperative time-series data, deep learning approaches are well-suited to incorporate time-varying and non-linear data with complex interactions. Blood lactate concentration is one important clinical marker that can reflect the adequacy of systemic perfusion during cardiac surgery. During cardiac surgery and cardiopulmonary bypass, minute-level data is available on key parameters that affect perfusion. The goal of this study was to use machine learning and deep learning approaches to predict maximum blood lactate concentrations after cardiac surgery. We hypothesized that models using minute-level intraoperative data as inputs would have the best predictive performance.

Methods

Adults who

SUBMITTER: Kobayashi Y 

PROVIDER: S-EPMC10543087 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature