Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review

TL;DR

This review evaluates innovative machine learning and biomarker-based methods for early ICU mortality prediction within the first 24 hours, aiming to improve accuracy and clinical decision-making.

Contribution

It systematically benchmarks recent advancements in data-driven approaches for early ICU mortality prediction, highlighting novel methodologies and data integration strategies.

Findings

Machine learning models outperform traditional scoring systems.

Biomarker integration enhances predictive accuracy.

Diverse data types improve early mortality prediction.

Abstract

The intensive care unit (ICU) manages critically ill patients, many of whom face a high risk of mortality. Early and accurate prediction of in-hospital mortality within the first 24 hours of ICU admission is crucial for timely clinical interventions, resource optimization, and improved patient outcomes. Traditional scoring systems, while useful, often have limitations in predictive accuracy and adaptability. Objective: This review aims to systematically evaluate and benchmark innovative methodologies that leverage data available within the first day of ICU admission for predicting in-hospital mortality. We focus on advancements in machine learning, novel biomarker applications, and the integration of diverse data types.