# Comparing the performance of radiomics, nomograms, machine learning, and large language models in predicting 28-day mortality in severe community-acquired pneumonia patients

## TL;DR

This study compares AI methods like radiomics, machine learning, and large language models to predict 28-day mortality in severe pneumonia patients.

## Contribution

The novel integration of LLMs with radiomics and machine learning for mortality prediction in SCAP patients is introduced.

## Key findings

- Clinical-radiomics models achieved high accuracy (AUC 0.92) in predicting mortality.
- XGBoost machine learning model performed well (AUC 0.90) with radiomic features being key predictors.
- LLMs like ChatGPT showed potential (AUC 0.78) when combined with clinical and radiomic data.

## Abstract

Severe community-acquired pneumonia (SCAP) is a significant global health challenge due to its high mortality. Despite advances, early diagnosis and effective management remain critical. Tools like radiomics analyze imaging data for risk assessment, while machine learning and nomograms aid in personalized treatment. Large language models (LLMs) enhance clinical decision-making by analyzing data and supporting care strategies. This study integrates these methods to predict 28-day mortality in SCAP patients.

A cohort of 599 patients diagnosed with severe community-acquired pneumonia (SCAP), including 316 males and 283 females, from Shanghai East Hospital and Xiamen Humanity Hospital were enrolled in this study. High-resolution lung CT scans were used to segment three-dimensional regions of interest, from which 1,050 radiomic features were extracted. The dataset was divided into a training set (80%) and an independent test set (20%), and k-fold cross-validation was applied to optimize model performance. To address class imbalance, the SMOTE oversampling technique was employed. The study integrated radiomics, nomograms, seven machine learning models, and five LLMs to predict the 28-day mortality risk in SCAP patients. SHAP values were utilized to enhance the interpretability of feature contributions. Not only that, this study integrates the prior knowledge provided by LLMs, processed through an embedding layer, with data-driven feature learning in the main network, and dynamically fuses their outputs using a bias network with a gating mechanism, thereby improving the accuracy and interpretability of LLMs in predicting 28-day mortality risk for SCAP patients.

Key predictors of 28-day mortality included inflammatory markers, cytokines, age, CRP, and oxygenation index. Clinical-Radiomics models achieved strong accuracy (AUC 0.92). Machine learning models, particularly XGBoost (AUC 0.90), were highly effective, with SHAP analysis emphasizing radscore’s importance. LLMs like Chatgpt also performed well (AUC 0.78), showcasing the potential of integrating clinical, radiomic, and AI-driven approaches.

This study demonstrates the effectiveness of radiomics, machine learning, and LLMs to predict SCAP outcomes. Models like XGBoost achieved superior accuracy, while SHAP analysis improved interpretability. These advancements highlight the potential for enhanced SCAP prognosis and personalized care strategies.