Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department
Abstract This study aimed to develop a machine learning-based clinical decision support system for emergency departments based on the decision-making framework of physicians. We extracted 27 fixed and 93 observation features using data on vital signs, mental status, laboratory results, and electroca...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-35617-3 |
| _version_ | 1827939992370937856 |
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| author | Arom Choi So Yeon Choi Kyungsoo Chung Hyun Soo Chung Taeyoung Song Byunghun Choi Ji Hoon Kim |
| author_facet | Arom Choi So Yeon Choi Kyungsoo Chung Hyun Soo Chung Taeyoung Song Byunghun Choi Ji Hoon Kim |
| author_sort | Arom Choi |
| collection | DOAJ |
| description | Abstract This study aimed to develop a machine learning-based clinical decision support system for emergency departments based on the decision-making framework of physicians. We extracted 27 fixed and 93 observation features using data on vital signs, mental status, laboratory results, and electrocardiograms during emergency department stay. Outcomes included intubation, admission to the intensive care unit, inotrope or vasopressor administration, and in-hospital cardiac arrest. eXtreme gradient boosting algorithm was used to learn and predict each outcome. Specificity, sensitivity, precision, F1 score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve were assessed. We analyzed 303,345 patients with 4,787,121 input data, resampled into 24,148,958 1 h-units. The models displayed a discriminative ability to predict outcomes (AUROC > 0.9), and the model with lagging 6 and leading 0 displayed the highest value. The AUROC curve of in-hospital cardiac arrest had the smallest change, with increased lagging for all outcomes. With inotropic use, intubation, and intensive care unit admission, the range of AUROC curve change with the leading 6 was the highest according to different amounts of previous information (lagging). In this study, a human-centered approach to emulate the clinical decision-making process of emergency physicians has been adopted to enhance the use of the system. Machine learning-based clinical decision support systems customized according to clinical situations can help improve the quality of care. |
| first_indexed | 2024-03-13T09:02:59Z |
| format | Article |
| id | doaj.art-b740ad01f7e94b0ea9945a9038ae7e97 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T09:02:59Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b740ad01f7e94b0ea9945a9038ae7e972023-05-28T11:12:56ZengNature PortfolioScientific Reports2045-23222023-05-0113111010.1038/s41598-023-35617-3Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency departmentArom Choi0So Yeon Choi1Kyungsoo Chung2Hyun Soo Chung3Taeyoung Song4Byunghun Choi5Ji Hoon Kim6Department of Emergency Medicine, Yonsei University College of MedicineDepartment of Emergency Medicine, Yonsei University College of MedicineDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Yonsei University College of MedicineDepartment of Emergency Medicine, Yonsei University College of MedicineLG ElectronicsLG ElectronicsDepartment of Emergency Medicine, Yonsei University College of MedicineAbstract This study aimed to develop a machine learning-based clinical decision support system for emergency departments based on the decision-making framework of physicians. We extracted 27 fixed and 93 observation features using data on vital signs, mental status, laboratory results, and electrocardiograms during emergency department stay. Outcomes included intubation, admission to the intensive care unit, inotrope or vasopressor administration, and in-hospital cardiac arrest. eXtreme gradient boosting algorithm was used to learn and predict each outcome. Specificity, sensitivity, precision, F1 score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve were assessed. We analyzed 303,345 patients with 4,787,121 input data, resampled into 24,148,958 1 h-units. The models displayed a discriminative ability to predict outcomes (AUROC > 0.9), and the model with lagging 6 and leading 0 displayed the highest value. The AUROC curve of in-hospital cardiac arrest had the smallest change, with increased lagging for all outcomes. With inotropic use, intubation, and intensive care unit admission, the range of AUROC curve change with the leading 6 was the highest according to different amounts of previous information (lagging). In this study, a human-centered approach to emulate the clinical decision-making process of emergency physicians has been adopted to enhance the use of the system. Machine learning-based clinical decision support systems customized according to clinical situations can help improve the quality of care.https://doi.org/10.1038/s41598-023-35617-3 |
| spellingShingle | Arom Choi So Yeon Choi Kyungsoo Chung Hyun Soo Chung Taeyoung Song Byunghun Choi Ji Hoon Kim Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department Scientific Reports |
| title | Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department |
| title_full | Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department |
| title_fullStr | Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department |
| title_full_unstemmed | Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department |
| title_short | Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department |
| title_sort | development of a machine learning based clinical decision support system to predict clinical deterioration in patients visiting the emergency department |
| url | https://doi.org/10.1038/s41598-023-35617-3 |
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