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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 hea...

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Autores principales: Hsu, Chun-Chuan, Chu, Cheng-C.J., Lin, Ching-Heng, Huang, Chien-Hsiung, Ng, Chip-Jin, Lin, Guan-Yu, Chiou, Meng-Jiun, Lo, Hsiang-Yun, Chen, Shou-Yen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8775134/
https://www.ncbi.nlm.nih.gov/pubmed/35054249
http://dx.doi.org/10.3390/diagnostics12010082
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author Hsu, Chun-Chuan
Chu, Cheng-C.J.
Lin, Ching-Heng
Huang, Chien-Hsiung
Ng, Chip-Jin
Lin, Guan-Yu
Chiou, Meng-Jiun
Lo, Hsiang-Yun
Chen, Shou-Yen
author_facet Hsu, Chun-Chuan
Chu, Cheng-C.J.
Lin, Ching-Heng
Huang, Chien-Hsiung
Ng, Chip-Jin
Lin, Guan-Yu
Chiou, Meng-Jiun
Lo, Hsiang-Yun
Chen, Shou-Yen
author_sort Hsu, Chun-Chuan
collection PubMed
description 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.
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spelling pubmed-87751342022-01-21 A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain Hsu, Chun-Chuan Chu, Cheng-C.J. Lin, Ching-Heng Huang, Chien-Hsiung Ng, Chip-Jin Lin, Guan-Yu Chiou, Meng-Jiun Lo, Hsiang-Yun Chen, Shou-Yen Diagnostics (Basel) Article 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. MDPI 2021-12-30 /pmc/articles/PMC8775134/ /pubmed/35054249 http://dx.doi.org/10.3390/diagnostics12010082 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hsu, Chun-Chuan
Chu, Cheng-C.J.
Lin, Ching-Heng
Huang, Chien-Hsiung
Ng, Chip-Jin
Lin, Guan-Yu
Chiou, Meng-Jiun
Lo, Hsiang-Yun
Chen, Shou-Yen
A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain
title A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain
title_full A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain
title_fullStr A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain
title_full_unstemmed A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain
title_short A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain
title_sort machine learning model for predicting unscheduled 72 h return visits to the emergency department by patients with abdominal pain
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8775134/
https://www.ncbi.nlm.nih.gov/pubmed/35054249
http://dx.doi.org/10.3390/diagnostics12010082
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