A machine learning model for predicting severity-adjusted in-hospital mortality in pneumonia patients.

OBJECTIVE

This study aims to develop a customized severity adjustment tool for hospital deaths in pneumonia patients considering characteristics of Korean discharged patients using representative data from the Korea Disease Control and Prevention Agency's Korea National Hospital Discharge In-Depth Injury Survey (KNHDIS).

METHODS

We analyzed 46,286 cases of pneumonia hospitalization among KNHDIS data from 2013 to 2022 and developed a model after adjusting for the severity of comorbidities using SAS and Python programs.

RESULTS

Analysis results showed that among three complication adjustment tools, including the existing complication index K-CCI (Korean-Charlson Comorbidity Index) and newly developed m-K-CCI (modified-Korean-Charlson Comorbidity Index) and m-K-CCS (modified-Korean-Clinical Classification Software), m-K-CCS was the best. For model development and evaluation, least absolute shrinkage and selection operator (LASSO), logistic regression, classification and regression tree (CART), random forests, gradient-boosted model (GBM), and artificial neural network (ANN) analyses were performed. Analysis of the validation dataset showed that GBM's m-K-CCS had the highest AUC value of 0.910.

CONCLUSION

These results suggest that further research is needed on models that adjust for the severity of comorbidities for each diagnosis to more accurately predict health outcomes.