J Korean Soc Emerg Med.  2018 Oct;29(5):455-464. 10.0000/jksem.2018.29.5.455.

Predicting the mortality of pneumonia patients visiting the emergency department through machine learning

• Bae Y
• Moon HK
• Kim SH

• KMID: 2435470
• DOI: http://doi.org/10.0000/jksem.2018.29.5.455

Abstract

OBJECTIVE
Machine learning is not yet widely used in the medical field. Therefore, this study was conducted to compare the performance of preexisting severity prediction models and machine learning based models (random forest [RF], gradient boosting [GB]) for mortality prediction in pneumonia patients.
METHODS
We retrospectively collected data from patients who visited the emergency department of a tertiary training hospital in Seoul, Korea from January to March of 2015. The Pneumonia Severity Index (PSI) and Sequential Organ Failure Assessment (SOFA) scores were calculated for both groups and the area under the curve (AUC) for mortality prediction was computed. For the RF and GB models, data were divided into a test set and a validation set by the random split method. The training set was learned in RF and GB models and the AUC was obtained from the validation set. The mean AUC was compared with the other two AUCs.
RESULTS
Of the 536 investigated patients, 395 were enrolled and 41 of them died. The AUC values of PSI and SOFA scores were 0.799 (0.737-0.862) and 0.865 (0.811-0.918), respectively. The mean AUC values obtained by the RF and GB models were 0.928 (0.899-0.957) and 0.919 (0.886-0.952), respectively. There were significant differences between preexisting severity prediction models and machine learning based models (P < 0.001).
CONCLUSION
Classification through machine learning may help predict the mortality of pneumonia patients visiting the emergency department.

Keyword

Pneumonia; Mortality; Machine learning; Emergency department

MeSH Terms

Area Under Curve
Classification
Emergencies*
Emergency Service, Hospital*
Forests
Humans
Korea
Machine Learning*
Methods
Mortality*
Pneumonia*
Retrospective Studies
Seoul