Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jun 16:11:20552076251351467.
doi: 10.1177/20552076251351467. eCollection 2025 Jan-Dec.

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

Jong-Ho Park  1 , Jihye Lim  2
Affiliations

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

Jong-Ho Park et al. Digit Health. .

Abstract

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.

Keywords: Charlson comorbidity index (CCI); Pneumonia; big data; comorbidity; machine learning.

PubMed Disclaimer

Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Study design and data.
Figure 2.
Figure 2.
Receiver of operating characteristic (ROC) curves for various machine learning techniques.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Campbell MJ, Jacques RM, Fotheringham J, et al. Developing a summary hospital mortality index: retrospective analysis in English hospitals over five years. Br Med J 2012; 344: 1–11. - PMC - PubMed
    1. Information CI for H. HSMR: a new approach for measuring hospital mortality trends in Canada. Canadian Institute for Health Information, 2007.
    1. Kim K-H, Ahn L-S. A comparative study on comorbidity measurements with lookback period using health insurance database: focused on patients who underwent percutaneous coronary intervention. J Prev Med Pub Health 2009; 42: 267–273. - PubMed
    1. Kim KH. Comorbidity adjustment in health insurance claim database. Health Policy and Management 2016; 26: 71–78.
    1. Charlson ME, Carrozzino D, Guidi J, et al. Charlson comorbidity index: a critical review of clinimetric properties. Psychother Psychosom 2022; 91: 8–35. - PubMed

LinkOut - more resources