Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review
Abstract Background In Emergency Departments (EDs), triage is crucial for determining patient severity and prioritizing care, typically using the Manchester Triage Scale (MTS). Traditional triage systems, reliant on human judgment, are prone to under-triage and over-triage, resulting in variability,...
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BMC
2024-11-01
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| Series: | BMC Emergency Medicine |
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| Online Access: | https://doi.org/10.1186/s12873-024-01135-2 |
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| author | Bruno Matos Porto |
| author_facet | Bruno Matos Porto |
| author_sort | Bruno Matos Porto |
| collection | DOAJ |
| description | Abstract Background In Emergency Departments (EDs), triage is crucial for determining patient severity and prioritizing care, typically using the Manchester Triage Scale (MTS). Traditional triage systems, reliant on human judgment, are prone to under-triage and over-triage, resulting in variability, bias, and incorrect patient classification. Studies suggest that Machine Learning (ML) and Natural Language Processing (NLP) could enhance triage accuracy and consistency. This review analyzes studies on ML and/or NLP algorithms for ED patient triage. Methods Following Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we conducted a systematic review across five databases: Web of Science, PubMed, Scopus, IEEE Xplore, and ACM Digital Library, from their inception of each database to October 2023. The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Only articles employing at least one ML and/or NLP method for patient triage classification were included. Results Sixty studies covering 57 ML algorithms were included. Logistic Regression (LR) was the most used model, while eXtreme Gradient Boosting (XGBoost), decision tree-based algorithms with Gradient Boosting (GB), and Deep Neural Networks (DNNs) showed superior performance. Frequent predictive variables included demographics and vital signs, with oxygen saturation, chief complaints, systolic blood pressure, age, and mode of arrival being the most retained. The ML algorithms showed significant bias risk due to critical bias assessment in classification models. Conclusion NLP methods improved ML algorithms' classification capability using triage nursing and medical notes and structured clinical data compared to algorithms using only structured data. Feature engineering (FE) and class imbalance correction methods enhanced ML workflows' performance, but FE and eXplainable Artificial Intelligence (XAI) were underexplored in this field. Registration and funding. This systematic review has been registered (registration number: CRD42024604529) in the International Prospective Register of Systematic Reviews (PROSPERO) and can be accessed online at the following URL: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=604529 . Funding for this work was provided by the National Council for Scientific and Technological Development (CNPq), Brazil. |
| format | Article |
| id | doaj-art-4999964658c64e079d32b00a657522ef |
| institution | OA Journals |
| issn | 1471-227X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Emergency Medicine |
| spelling | doaj-art-4999964658c64e079d32b00a657522ef2025-08-20T02:32:57ZengBMCBMC Emergency Medicine1471-227X2024-11-0124112910.1186/s12873-024-01135-2Improving triage performance in emergency departments using machine learning and natural language processing: a systematic reviewBruno Matos Porto0Industrial Engineering Department, Federal University of Rio Grande do SulAbstract Background In Emergency Departments (EDs), triage is crucial for determining patient severity and prioritizing care, typically using the Manchester Triage Scale (MTS). Traditional triage systems, reliant on human judgment, are prone to under-triage and over-triage, resulting in variability, bias, and incorrect patient classification. Studies suggest that Machine Learning (ML) and Natural Language Processing (NLP) could enhance triage accuracy and consistency. This review analyzes studies on ML and/or NLP algorithms for ED patient triage. Methods Following Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we conducted a systematic review across five databases: Web of Science, PubMed, Scopus, IEEE Xplore, and ACM Digital Library, from their inception of each database to October 2023. The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Only articles employing at least one ML and/or NLP method for patient triage classification were included. Results Sixty studies covering 57 ML algorithms were included. Logistic Regression (LR) was the most used model, while eXtreme Gradient Boosting (XGBoost), decision tree-based algorithms with Gradient Boosting (GB), and Deep Neural Networks (DNNs) showed superior performance. Frequent predictive variables included demographics and vital signs, with oxygen saturation, chief complaints, systolic blood pressure, age, and mode of arrival being the most retained. The ML algorithms showed significant bias risk due to critical bias assessment in classification models. Conclusion NLP methods improved ML algorithms' classification capability using triage nursing and medical notes and structured clinical data compared to algorithms using only structured data. Feature engineering (FE) and class imbalance correction methods enhanced ML workflows' performance, but FE and eXplainable Artificial Intelligence (XAI) were underexplored in this field. Registration and funding. This systematic review has been registered (registration number: CRD42024604529) in the International Prospective Register of Systematic Reviews (PROSPERO) and can be accessed online at the following URL: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=604529 . Funding for this work was provided by the National Council for Scientific and Technological Development (CNPq), Brazil.https://doi.org/10.1186/s12873-024-01135-2Triage of patientsTriage systemsArtificial intelligenceFeature engineering |
| spellingShingle | Bruno Matos Porto Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review BMC Emergency Medicine Triage of patients Triage systems Artificial intelligence Feature engineering |
| title | Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review |
| title_full | Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review |
| title_fullStr | Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review |
| title_full_unstemmed | Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review |
| title_short | Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review |
| title_sort | improving triage performance in emergency departments using machine learning and natural language processing a systematic review |
| topic | Triage of patients Triage systems Artificial intelligence Feature engineering |
| url | https://doi.org/10.1186/s12873-024-01135-2 |
| work_keys_str_mv | AT brunomatosporto improvingtriageperformanceinemergencydepartmentsusingmachinelearningandnaturallanguageprocessingasystematicreview |