Guardado en:
| Autores principales: | , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2021
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2111.11017 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912170672914432 |
|---|---|
| author | Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Rajnthern, Logasan S/O Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan |
| author_facet | Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Rajnthern, Logasan S/O Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan |
| contents | 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 predictive models and decision support systems to address these challenges. To date, however, there are no widely accepted benchmark ED triage prediction models based on large-scale public EHR data. An open-source benchmarking platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400,000 ED visits from 2011 to 2019. We introduced three ED-based outcomes (hospitalization, critical outcomes, and 72-hour ED reattendance) and implemented a variety of popular methodologies, ranging from machine learning methods to clinical scoring systems. We evaluated and compared the performance of these methods against benchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-ED data access to perform the same steps in data processing, benchmark model building, and experiments. This study provides future researchers with insights, suggestions, and protocols for managing raw data and developing risk triaging tools for emergency care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_11017 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Benchmarking emergency department triage prediction models with machine learning and large public electronic health records Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Rajnthern, Logasan S/O Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan Machine Learning 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 predictive models and decision support systems to address these challenges. To date, however, there are no widely accepted benchmark ED triage prediction models based on large-scale public EHR data. An open-source benchmarking platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400,000 ED visits from 2011 to 2019. We introduced three ED-based outcomes (hospitalization, critical outcomes, and 72-hour ED reattendance) and implemented a variety of popular methodologies, ranging from machine learning methods to clinical scoring systems. We evaluated and compared the performance of these methods against benchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-ED data access to perform the same steps in data processing, benchmark model building, and experiments. This study provides future researchers with insights, suggestions, and protocols for managing raw data and developing risk triaging tools for emergency care. |
| title | Benchmarking emergency department triage prediction models with machine learning and large public electronic health records |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2111.11017 |