Benchmarking emergency department prediction models with machine learning and public electronic health records
The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by t...
| Main Authors: | , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/171029 |
| _version_ | 1811676322350497792 |
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| author | Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Logasan S/O Rajnthern Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Logasan S/O Rajnthern Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan |
| author_sort | Xie, Feng |
| collection | NTU |
| description | The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop prediction models and decision support systems to address these challenges. To date, there is no widely accepted clinical prediction benchmark related to the ED based on large-scale public EHRs. An open-source benchmark data platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. Based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we created a benchmark dataset and proposed three clinical prediction benchmarks. This study provides future researchers with insights, suggestions, and protocols for managing data and developing predictive tools for emergency care. |
| first_indexed | 2024-10-01T02:19:38Z |
| format | Journal Article |
| id | ntu-10356/171029 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:19:38Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1710292023-10-13T15:40:21Z Benchmarking emergency department prediction models with machine learning and public electronic health records Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Logasan S/O Rajnthern Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Machine Learning Electronic Health Records The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop prediction models and decision support systems to address these challenges. To date, there is no widely accepted clinical prediction benchmark related to the ED based on large-scale public EHRs. An open-source benchmark data platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. Based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we created a benchmark dataset and proposed three clinical prediction benchmarks. This study provides future researchers with insights, suggestions, and protocols for managing data and developing predictive tools for emergency care. Published version 2023-10-13T08:13:18Z 2023-10-13T08:13:18Z 2022 Journal Article Xie, F., Zhou, J., Lee, J. W., Tan, M., Li, S., Logasan S/O Rajnthern, Chee, M. L., Chakraborty, B., Wong, A. I., Dagan, A., Ong, M. E. H., Gao, F. & Liu, N. (2022). Benchmarking emergency department prediction models with machine learning and public electronic health records. Scientific Data, 9(1), 658-. https://dx.doi.org/10.1038/s41597-022-01782-9 2052-4463 https://hdl.handle.net/10356/171029 10.1038/s41597-022-01782-9 36302776 2-s2.0-85140797819 1 9 658 en Scientific Data © 2022 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Machine Learning Electronic Health Records Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Logasan S/O Rajnthern Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan Benchmarking emergency department prediction models with machine learning and public electronic health records |
| title | Benchmarking emergency department prediction models with machine learning and public electronic health records |
| title_full | Benchmarking emergency department prediction models with machine learning and public electronic health records |
| title_fullStr | Benchmarking emergency department prediction models with machine learning and public electronic health records |
| title_full_unstemmed | Benchmarking emergency department prediction models with machine learning and public electronic health records |
| title_short | Benchmarking emergency department prediction models with machine learning and public electronic health records |
| title_sort | benchmarking emergency department prediction models with machine learning and public electronic health records |
| topic | Engineering::Electrical and electronic engineering Machine Learning Electronic Health Records |
| url | https://hdl.handle.net/10356/171029 |
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