| 기계학습모델을 통한 응급실 폐렴환자의 사망예측 모델과 기존 예측 모델의 비교 |
배 열
, 문형기
, 김수현
|
| 가톨릭대학교 의과대학 응급의학교실 |
| Predicting the mortality of pneumonia patients visiting the emergency department through machine learning |
| Yeol Bae
, Hyung Ki Moon
, Soo Hyun Kim
|
| Department of Emergency Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea |
| Correspondence |
Hyung Ki Moon ,Tel: 02-2258-2002, Fax: 02-2258-1997, Email: 1121111@hanmail.net,
|
|
|
Received: June 25, 2018; Revised: August 2, 2018 Accepted: August 20, 2018. Published online: October 31, 2018. |
|
|
|
| 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.
Method:
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 an 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. |
| Key words:
Pneumonia; Mortality; Machine learning; Emergency department |
|
PDF Links
Full text via DOI 
Download Citation 
