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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8775134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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