Chun-Chuan Hsu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Chun-Chuan Hsu has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Cheng-Chi James Chu
Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou, Taoyuan City, Taiwan
1 shared publication - Chien-Hsiung Huang
Department of Emergency Medicine, Chang Gung Memorial Hospital, Chang Gung Medical Foundation, Taoyuan, Taiwan
1 shared publication - Ching-Heng Lin
Center for Artificial Intelligence in Medicine, Guishan Dist, Chang Gung Memorial Hospital, Taoyuan City 333, Taiwan
1 shared publication - Chip-Jin Ng
Department of Emergency Medicine Linkou Chang Gung Memorial Hospital Taoyuan City Taiwan
1 shared publication - Guan-Yu Lin
Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou, Taoyuan City 333, Taiwan
1 shared publication - Hsiang-Yun Lo
Department of Emergency Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
1 shared publication
Latest Publications
- Open Access
- 30 December 2021
- Published by MDPI AG in Diagnostics
- Vol. 12 (1) , 82
A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain
Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69–0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69–0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.
- 30 December 2021
- Published by MDPI AG in Diagnostics
- Vol. 12 (1) , 82
Open AccessA Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain
Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69–0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69–0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.