Enhancing mortality prediction in cardiac arrest ICU patients through meta-modeling of structured clinical data from MIMIC-IV

TL;DR

This study improves in-hospital mortality prediction for ICU cardiac arrest patients by combining structured clinical data with unstructured textual information using advanced machine learning techniques, achieving higher accuracy and clinical utility.

Contribution

It introduces a novel meta-modeling approach that integrates textual and structured data, significantly enhancing mortality prediction accuracy in ICU settings.

Findings

Combined structured and textual data increased AUC to 0.918

Textual features provided substantial prognostic value

Model demonstrated clinical utility across thresholds

Abstract

Accurate early prediction of in-hospital mortality in intensive care units (ICUs) is essential for timely clinical intervention and efficient resource allocation. This study develops and evaluates machine learning models that integrate both structured clinical data and unstructured textual information, specifically discharge summaries and radiology reports, from the MIMIC-IV database. We used LASSO and XGBoost for feature selection, followed by a multivariate logistic regression trained on the top features identified by both models. Incorporating textual features using TF-IDF and BERT embeddings significantly improved predictive performance. The final logistic regression model, which combined structured and textual input, achieved an AUC of 0.918, compared to 0.753 when using structured data alone, a relative improvement 22%. The analysis of the decision curve demonstrated a superior standardized net benefit in a wide range of threshold probabilities (0.2-0.8), confirming the clinical utility of the model. These results underscore the added prognostic value of unstructured clinical notes and support their integration into interpretable feature-driven risk prediction models for ICU patients.