Academic Journal
Improved interpretable machine learning emergency department triage tool addressing class imbalance
| العنوان: | Improved interpretable machine learning emergency department triage tool addressing class imbalance |
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| المؤلفون: | Clarisse SJ Look, Salinelat Teixayavong, Therese Djärv, Andrew FW Ho, Kenneth BK Tan, Marcus EH Ong |
| المصدر: | Digital Health, Vol 10 (2024) |
| بيانات النشر: | SAGE Publishing, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7 |
| الوصف: | Objective The Score for Emergency Risk Prediction (SERP) is a novel mortality risk prediction score which leverages machine learning in supporting triage decisions. In its derivation study, SERP-2d, SERP-7d and SERP-30d demonstrated good predictive performance for 2-day, 7-day and 30-day mortality. However, the dataset used had significant class imbalance. This study aimed to determine if addressing class imbalance can improve SERP's performance, ultimately improving triage accuracy. Methods The Singapore General Hospital (SGH) emergency department (ED) dataset was used, which contains 1,833,908 ED records between 2008 and 2020. Records between 2008 and 2017 were randomly split into a training set (80%) and validation set (20%). The 2019 and 2020 records were used as test sets. To address class imbalance, we used random oversampling and random undersampling in the AutoScore-Imbalance framework to develop SERP+-2d, SERP+-7d, and SERP+-30d scores. The performance of SERP+, SERP, and the commonly used triage risk scores was compared. Results The developed SERP+ scores had five to six variables. The AUC of SERP+ scores (0.874 to 0.905) was higher than that of the corresponding SERP scores (0.859 to 0.894) on both test sets. This superior performance was statistically significant for SERP+-7d (2019: Z = −5.843, p |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2055-2076 20552076 |
| Relation: | https://doaj.org/toc/2055-2076 |
| DOI: | 10.1177/20552076241240910 |
| URL الوصول: | https://doaj.org/article/55489b6d6e57423f860717001b94ef4f |
| رقم الانضمام: | edsdoj.55489b6d6e57423f860717001b94ef4f |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20552076 |
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| DOI: | 10.1177/20552076241240910 |