Predicting the mortality of pneumonia patients visiting the emergency department through machine learning.
- Author:
Yeol BAE
1
;
Hyung Ki MOON
;
Soo Hyun KIM
Author Information
1. Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea. 1121111@hanmail.net
- Publication Type:Original Article
- Keywords:
Pneumonia;
Mortality;
Machine learning;
Emergency department
- MeSH:
Area Under Curve;
Classification;
Emergencies*;
Emergency Service, Hospital*;
Forests;
Humans;
Korea;
Machine Learning*;
Methods;
Mortality*;
Pneumonia*;
Retrospective Studies;
Seoul
- From:Journal of the Korean Society of Emergency Medicine
2018;29(5):455-464
- CountryRepublic of Korea
- Language:Korean
-
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.
