Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes

TL;DR

This study develops machine learning models combining structured data and clinical notes from ICU patients to predict early mortality in sepsis, aiming to improve clinical decision-making and patient outcomes.

Contribution

It introduces an integrated approach using both structured features and unstructured clinical notes for early mortality prediction in sepsis patients.

Findings

Integrated models achieved an F-measure of 0.512.

Combining structured and unstructured data improves prediction accuracy.

Models can assist clinicians in early risk assessment upon ICU admission.

Abstract

Sepsis is an important cause of mortality, especially in intensive care unit (ICU) patients. Developing novel methods to identify early mortality is critical for improving survival outcomes in sepsis patients. Using the MIMIC-III database, we integrated demographic data, physiological measurements and clinical notes. We built and applied several machine learning models to predict the risk of hospital mortality and 30-day mortality in sepsis patients. From the clinical notes, we generated clinically meaningful word representations and embeddings. Supervised learning classifiers and a deep learning architecture were used to construct prediction models. The configurations that utilized both structured and unstructured clinical features yielded competitive F-measure of 0.512. Our results showed that the approaches integrating both structured and unstructured clinical features can be effectively applied to assist clinicians in identifying the risk of mortality in sepsis patients upon admission to the ICU.