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請使用永久網址來引用或連結此文件:
https://irlib.pccu.edu.tw/handle/987654321/48908
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| 題名: | An Innovative Scoring System for Predicting Major Adverse Cardiac Events in Patients With Chest Pain Based on Machine Learning |
| 作者: | Wu, CC (Wu, Chieh-Chen)
Hsu, WD (Hsu, Wen-Ding)
Wang, YC (Wang, Yao-Chin)
Kung, WM (Kung, Woon-Man)
Tzeng, IS (Tzeng, I-Shiang)
Huang, CW (Huang, Chih-Wei)
Huang, CY (Huang, Chu-Ya)
Li, YC (Li, Yu-Chuan) |
| 貢獻者: | 運健系 |
| 關鍵詞: | hest pain
emergency department
scoring system
major adverse cardiac events
ST-elevation myocardial infarction
non-ST-elevation myocardial infarction
risk stratification
machine learning |
| 日期: | 2020 |
| 上傳時間: | 2020-12-14 11:04:03 (UTC+8) |
| 摘要: | Chest pain is a common complaint in the emergency department, but this may prevent a diagnosis of major adverse cardiac events, a composite of all-cause mortality associated with cardiovascular-related illnesses. To determine potential predictors of major adverse cardiac events in Taiwan, a pilot study was performed, involving the data from 268 patients with major adverse cardiac events, which was by an artificial neural network method. Nine biomarkers were selected for identifying non-ST-elevation myocardial infarction from common chest pain patients. By using a machine learning-based feature selection technique, five biomarkers were chosen from a set of 37 candidate variables. A full and a reduced risk stratification model were built. The full model was based on the characteristics of both invasive (i.e., creatinine and troponin I) and non-invasive (i.e., age, coronary artery disease risk factors, and corrected QT interval) variables, and the reduced model was based only on non-invasive variable characteristics. The full model showed a sensitivity of 0.948 and a specificity of 0.546 when the cutoff was set at 2 points, with a missed major adverse cardiac events rate of 1.32%, a positive predictive value of 0.228, and a negative predictive value of 0.987. High performance was also obtained with the five major biomarkers in the predictor built by the machine learning algorithm. The full model had the highest performance, but the reduced model can be applied as a quick and reasonably performing diagnostic tool. |
| 關聯: | IEEE ACCESS 卷冊: 8 頁數: 124076-124083 |
| 顯示於類別: | [運動與健康促進學系] 期刊論文
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