Top

Published in:

Open Access 01-12-2024 | Triage | Systematic Review

Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review

Author: Bruno Matos Porto

Published in: BMC Emergency Medicine | Issue 1/2024

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.

Available only for authorised users

R. Sánchez-Salmerón et al., “Machine learning methods applied to triage in emergency services: A systematic review,” Int. Emerg. Nurs., vol. 60, no. August 2021, p. 101109, Jan. 2022, https://doi.org/10.1016/j.ienj.2021.101109.

M. Fernandes, S. M. Vieira, F. Leite, C. Palos, S. Finkelstein, and J. M. C. Sousa, “Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review,” Artif. Intell. Med., vol. 102, no. February 2019, p. 101762, Jan. 2020, https://doi.org/10.1016/j.artmed.2019.101762.

Kwon J, Lee Y, Lee Y, Lee S, Park H, Park J. Validation of deep-learning-based triage and acuity score using a large national dataset. PLoS ONE. 2018;13(10). https://doi.org/10.1371/journal.pone.0205836.

Zaboli A. Establishing a common ground: the future of triage systems. BMC Emerg Med. 2024;24(1):148. https://doi.org/10.1186/s12873-024-01070-2.CrossRefPubMedPubMedCentral

Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019;23(1):64. https://doi.org/10.1186/s13054-019-2351-7.CrossRefPubMedPubMedCentral

Choi SW, Ko T, Hong KJ, Kim KH. Machine learning-based prediction of korean triage and acuity scale level in emergency department patients. Healthc Inform Res. 2019;25(4):305. https://doi.org/10.4258/hir.2019.25.4.305.CrossRefPubMedPubMedCentral

Yu JY, Jeong GY, Jeong OS, Chang DK, Cha WC. Machine learning and initial nursing assessment-based triage system for emergency department. Healthc Inform Res. 2020;26(1):13. https://doi.org/10.4258/hir.2020.26.1.13.CrossRefPubMedPubMedCentral

H. Jiang et al., “Machine learning-based models to support decision-making in emergency department triage for patients with suspected cardiovascular disease,” Int. J. Med. Inform., vol. 145, no. October 2020, p. 104326, Jan. 2021, https://doi.org/10.1016/j.ijmedinf.2020.104326.

Liu Y, et al. Development and validation of a practical machine-learning triage algorithm for the detection of patients in need of critical care in the emergency department. Sci Rep. Dec.2021;11(1):24044. https://doi.org/10.1038/s41598-021-03104-2.CrossRefPubMedPubMedCentral

10.

Fernandes M, et al. Predicting intensive care unit admission among patients presenting to the emergency department using machine learning and natural language processing. PLoS ONE. 2020;15(3). https://doi.org/10.1371/journal.pone.0229331.

11.

Hinson JS, et al. Accuracy of emergency department triage using the emergency severity index and independent predictors of under-triage and over-triage in Brazil: a retrospective cohort analysis. Int J Emerg Med. 2018;11(1):3. https://doi.org/10.1186/s12245-017-0161-8.CrossRefPubMedPubMedCentral

12.

Z. Gao et al., “Developing and Validating an Emergency Triage Model Using Machine Learning Algorithms with Medical Big Data,” Risk Manag. Healthc. Policy, vol. Volume 15, no. July, pp. 1545–1551, Aug. 2022, https://doi.org/10.2147/RMHP.S355176.

13.

Joseph JW, et al. Deep-learning approaches to identify critically Ill patients at emergency department triage using limited information. J Am Coll Emerg Physicians Open. 2020;1(5):773–81. https://doi.org/10.1002/emp2.12218.CrossRefPubMedPubMedCentral

14.

Mistry B, et al. Accuracy and reliability of emergency department triage using the emergency severity index: an international multicenter assessment. Ann Emerg Med. 2018;71(5):581-587.e3. https://doi.org/10.1016/j.annemergmed.2017.09.036.CrossRefPubMed

15.

H. Elhaj, N. Achour, M. H. Tania, and K. Aciksari, “A comparative study of supervised machine learning approaches to predict patient triage outcomes in hospital emergency departments,” Array, vol. 17, no. October 2022, p. 100281, Mar. 2023, https://doi.org/10.1016/j.array.2023.100281.

16.

A. Zaboli et al., “Assessing triage efficiency in Italy: a comparative study using simulated cases among nurses,” Intern. Emerg. Med., no. 0123456789, Aug. 2024, https://doi.org/10.1007/s11739-024-03735-z.

17.

Levin S, et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Ann Emerg Med. 2018;71(5):565-574.e2. https://doi.org/10.1016/j.annemergmed.2017.08.005.CrossRefPubMed

18.

Miles J, Turner J, Jacques R, Williams J, Mason S. Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review. Diagn Progn Res. 2020;4(1):16. https://doi.org/10.1186/s41512-020-00084-1.CrossRefPubMedPubMedCentral

19.

Shafaf N, Malek H. Applications of machine learning approaches in emergency medicine; a review article. Arch Acad Emerg Med. 2019;7(1):34. https://doi.org/10.22037/aaem.v7i1.410.CrossRefPubMedPubMedCentral

20.

Wang S. Construct an optimal triage prediction model: a case study of the emergency department of a teaching hospital in Taiwan. J Med Syst. 2013;37(9968):2–11. https://doi.org/10.1007/s10916-013-9968-x.CrossRef

21.

Ruiz C, Tello I, Yoo SG. Improvement of the triage process using process automatization and machine learning. Int J Appl Eng Res. 2017;12(15):4989–99.

22.

Garmendia A, Rios SA, Lopez-Guede JM, Graña M. Triage prediction in pediatric patients with respiratory problems. Neurocomputing. 2019;326–327:161–7. https://doi.org/10.1016/j.neucom.2017.01.122.CrossRef

23.

Ong MEH, et al. Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score. Crit Care. 2012;16(3):R108. https://doi.org/10.1186/cc11396.CrossRefPubMedPubMedCentral

24.

Dugas AF, et al. An electronic emergency triage system to improve patient distribution by critical outcomes. J Emerg Med. Jun.2016;50(6):910–8. https://doi.org/10.1016/j.jemermed.2016.02.026.CrossRefPubMed

25.

Kim D, et al. A data-driven artificial intelligence model for remote triage in the prehospital environment. PLoS ONE. 2018;13(10):e0206006. https://doi.org/10.1371/journal.pone.0206006.CrossRefPubMedPubMedCentral

26.

G. Feretzakis et al., “Using Machine Learning for Predicting the Hospitalization of Emergency Department Patients,” in Studies in Health Technology and Informatics, 2022, pp. 405–408. https://doi.org/10.3233/SHTI220751.

27.

G. Feretzakis et al., “Prediction of Hospitalization Using Machine Learning for Emergency Department Patients,” in Studies in Health Technology and Informatics, 2022, pp. 145–146. https://doi.org/10.3233/SHTI220422.

28.

Feretzakis G, et al. Using machine learning techniques to predict hospital admission at the emergency department. J Crit Care Med. 2022;8(2):107–16. https://doi.org/10.2478/jccm-2022-0003.CrossRef

29.

G. Feretzakis et al., “Predicting Hospital Admission for Emergency Department Patients: A Machine Learning Approach,” in Studies in Health Technology and Informatics, 2022, pp. 297–300. https://doi.org/10.3233/SHTI210918.

30.

Wu TT, Zheng RF, Lin ZZ, Gong HR, Li H. A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department. BMC Emerg Med. 2021;21(1):112. https://doi.org/10.1186/s12873-021-00501-8.CrossRefPubMedPubMedCentral

31.

Zhang L, et al. Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest). BMC Emerg Med. 2022;22(1):26. https://doi.org/10.1186/s12873-022-00582-z.CrossRefPubMedPubMedCentral

32.

Karlsson A, Stassen W, Loutfi A, Wallgren U, Larsson E, Kurland L. Predicting mortality among septic patients presenting to the emergency department–a cross sectional analysis using machine learning. BMC Emerg Med. 2021;21(1):84. https://doi.org/10.1186/s12873-021-00475-7.CrossRefPubMedPubMedCentral

33.

Niemantsverdriet MSA, et al. A machine learning approach using endpoint adjudication committee labels for the identification of sepsis predictors at the emergency department. BMC Emerg Med. 2022;22(1):208. https://doi.org/10.1186/s12873-022-00764-9.CrossRefPubMedPubMedCentral

34.

Deina C, Fogliatto FS, da Silveira GJC, Anzanello MJ. Decision analysis framework for predicting no-shows to appointments using machine learning algorithms. BMC Health Serv Res. 2024;24(1):37. https://doi.org/10.1186/s12913-023-10418-6.CrossRefPubMedPubMedCentral

35.

Kim D, Chae J, Oh Y, Lee J, Kim IY. Automated remote decision-making algorithm as a primary triage system using machine learning techniques. Physiol Meas. 2021;42(2). https://doi.org/10.1088/1361-6579/abe524.

36.

De Hond A, et al. Machine learning for developing a prediction model of hospital admission of emergency department patients: Hype or hope? Int J Med Inform. 2021;152:104496. https://doi.org/10.1016/J.IJMEDINF.2021.104496.CrossRefPubMed

37.

Chen C-H, Hsieh J-G, Cheng S-L, Lin Y-L, Lin P-H, Jeng J-H. Emergency department disposition prediction using a deep neural network with integrated clinical narratives and structured data. Int J Med Inform. 2020;139(49). https://doi.org/10.1016/j.ijmedinf.2020.104146.

38.

T.-L. Chen, J. C. Chen, W.-H. Chang, W. Tsai, M.-C. Shih, and A. Wildan Nabila, “Imbalanced prediction of emergency department admission using natural language processing and deep neural network,” J. Biomed. Inform., vol. 133, no. August, p. 104171, Sep. 2022, https://doi.org/10.1016/j.jbi.2022.104171.

39.

M. Chen, T. Huang, T. Chen, P. Boonyarat, and C. Chang, “Clinical narrative-aware deep neural network for emergency department critical outcome prediction,” J. Biomed. Inform., vol. 138, no. January, p. 104284, 2023, https://doi.org/10.1016/j.jbi.2023.104284.

40.

Klug M, et al. A gradient boosting machine learning model for predicting early mortality in the emergency department triage: devising a nine-point triage score. J Gen Intern Med. 2020;35(1):220–7. https://doi.org/10.1007/s11606-019-05512-7.CrossRefPubMed

41.

Tang F, Xiao C, Wang F, Zhou J. Predictive modeling in urgent care: a comparative study of machine learning approaches. JAMIA Open. 2018;1(1):87–98. https://doi.org/10.1093/jamiaopen/ooy011.CrossRefPubMedPubMedCentral

42.

Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Informatics Assoc. 2011;18(5):544–51. https://doi.org/10.1136/amiajnl-2011-000464.CrossRef

43.

Klang E, et al. Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach. Sci Rep. 2021;11(1):1381. https://doi.org/10.1038/s41598-021-80985-3.CrossRefPubMedPubMedCentral

44.

Klang E, et al. A simple free-text-like method for extracting semi-structured data from electronic health records: exemplified in prediction of in-hospital mortality. Big Data Cogn Comput. 2021;5(3):40. https://doi.org/10.3390/bdcc5030040.CrossRef

45.

D. Kim, J. Oh, H. Im, M. Yoon, J. Park, and J. Lee, “Automatic Classification of the Korean Triage Acuity Scale in Simulated Emergency Rooms Using Speech Recognition and Natural Language Processing: a Proof of Concept Study,” J. Korean Med. Sci., vol. 36, no. 27, 2021, https://doi.org/10.3346/jkms.2021.36.e175.

46.

B. Wang, W. Li, A. Bradlow, E. Bazuaye, and A. T. Y. Chan, “Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning,” Decis. Support Syst., vol. 166, 2023, https://doi.org/10.1016/j.dss.2022.113899.

47.

Y. Xiao, J. Zhang, C. Chi, Y. Ma, and A. Song, “Criticality and clinical department prediction of ED patients using machine learning based on heterogeneous medical data,” Comput. Biol. Med., vol. 165, no. July, p. 107390, Oct. 2023, https://doi.org/10.1016/j.compbiomed.2023.107390.

48.

Sarbay İ, Berikol G, Özturan İ. Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study. Turkish J Emerg Med. 2023;23(3):156. https://doi.org/10.4103/tjem.tjem_79_23.CrossRef

49.

Zaboli A, Brigo F, Sibilio S, Mian M, Turcato G. Human intelligence versus Chat-GPT: who performs better in correctly classifying patients in triage? Am J Emerg Med. 2024;79:44–7. https://doi.org/10.1016/j.ajem.2024.02.008.CrossRefPubMed

50.

F. Gao, B. Boukebous, M. Pozzar, E. Alaoui, B. Sano, and S. Bayat-Makoei, “Predictive Models for Emergency Department Triage using Machine Learning: A Systematic Review,” Obstet. Gynecol. Res., vol. 05, no. 02, 2022, https://doi.org/10.26502/ogr085.

51.

M. Kuhn and K. Johnson, Feature Engineering and Selection: A Practical Approach for Predictive Models. Taylor & Francis Group, 2019. Available: https://bookdown.org/max/FES/

52.

A. Barredo Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Inf. Fusion, vol. 58, no. December 2019, pp. 82–115, Jun. 2020, https://doi.org/10.1016/j.inffus.2019.12.012.

53.

Wolff RF, et al. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51. https://doi.org/10.7326/M18-1376.CrossRefPubMed

54.

Saaiq M, Ashraf B. Modifying ‘Pico’ question into ‘Picos’ model for more robust and reproducible presentation of the methodology employed in a scientific study. World J Plast Surg. 2017;6(3):390–2.PubMedPubMedCentral

55.

M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” Int. J. Surg., vol. 88, no. March, p. 105906, Apr. 2021, https://doi.org/10.1016/j.ijsu.2021.105906.

56.

Fernandez-Felix BM, López-Alcalde J, Roqué M, Muriel A, Zamora J. CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models. BMC Med Res Methodol. 2023;23(1):44. https://doi.org/10.1186/s12874-023-01849-0.CrossRefPubMedPubMedCentral

57.

Abad-grau M, Ierache J, Cervino C, Sebastiani P. Evolution and challenges in the design of computational systems for triage assistance. J Biomed Inform. 2008;41:432–41. https://doi.org/10.1016/j.jbi.2008.01.007.CrossRefPubMedPubMedCentral

58.

Zmiri D, Shahar Y, Taieb-Maimon M. Classification of patients by severity grades during triage in the emergency department using data mining methods. J Eval Clin Pract. 2012;18(2):378–88. https://doi.org/10.1111/j.1365-2753.2010.01592.x.CrossRefPubMed

59.

Chonde SJ, Ashour OM, Nembhard DA, Kremer GEO. Model comparison in emergency severity index level prediction. Expert Syst Appl. 2013;40(17):6901–9. https://doi.org/10.1016/j.eswa.2013.06.026.CrossRef

60.

Azeez D, Ali MAM, Gan K, Saiboon I. Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department. Springerplus. 2013;2(416):1–10. https://doi.org/10.1186/2193-1801-2-416.CrossRef

61.

Goto T, Camargo CA, Faridi MK, Yun BJ, Hasegawa K. Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED. Am J Emerg Med. 2018;36(9):1650–4. https://doi.org/10.1016/j.ajem.2018.06.062.CrossRefPubMed

62.

Wolff P, Ríos SA, Graña M. Setting up standards: a methodological proposal for pediatric triage machine learning model construction based on clinical outcomes. Expert Syst Appl. 2019;138:112788. https://doi.org/10.1016/j.eswa.2019.07.005.CrossRef

63.

Goto T, Camargo CA, Faridi MK, Freishtat RJ, Hasegawa K. Machine learning-based prediction of clinical outcomes for children during emergency department triage. JAMA Netw Open. 2019;2(1):e186937. https://doi.org/10.1001/jamanetworkopen.2018.6937.CrossRefPubMedPubMedCentral

64.

Kwon J, Jeon K-H, Lee M, Kim K-H, Park J, Oh B-H. Deep learning algorithm to predict need for critical care in pediatric emergency departments. Pediatr Emerg Care. 2021;37(12):e988-94. https://doi.org/10.1097/PEC.0000000000001858.CrossRefPubMed

65.

Liu W, Wang Z, Liu X, Zeng N, Bell D. A novel particle swarm optimization approach for patient clustering from emergency departments. IEEE Trans Evol Comput. 2019;23(4):632–44. https://doi.org/10.1109/TEVC.2018.2878536.CrossRef

66.

Kang D-Y, et al. Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services. Scand J Trauma Resusc Emerg Med. 2020;28(1):17. https://doi.org/10.1186/s13049-020-0713-4.CrossRefPubMedPubMedCentral

67.

Fernandes M, et al. Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing. PLoS ONE. 2020;15(4):e0230876. https://doi.org/10.1371/journal.pone.0230876.CrossRefPubMedPubMedCentral

68.

C. Li et al., “Machine learning based early mortality prediction in the emergency department,” Int. J. Med. Inform., vol. 155, no. September, p. 104570, Nov. 2021, https://doi.org/10.1016/j.ijmedinf.2021.104570.

69.

Xie F, et al. Development and assessment of an interpretable machine learning triage tool for estimating mortality after emergency admissions. JAMA Netw Open. 2021;4(8):e2118467. https://doi.org/10.1001/jamanetworkopen.2021.18467.CrossRefPubMedPubMedCentral

70.

Ivanov O, et al. Improving ED emergency severity index acuity assignment using machine learning and clinical natural language processing. J Emerg Nurs. 2021;47(2):265-278.e7. https://doi.org/10.1016/j.jen.2020.11.001.CrossRefPubMed

71.

Heyming TW, Knudsen-Robbins C, Feaster W, Ehwerhemuepha L. Criticality index conducted in pediatric emergency department triage. Am J Emerg Med. 2021;48:209–17. https://doi.org/10.1016/j.ajem.2021.05.004.CrossRefPubMed

72.

Nguyen M, et al. Developing machine learning models to personalize care levels among emergency room patients for hospital admission. J Am Med Informatics Assoc. 2021;28(11):2423–32. https://doi.org/10.1093/jamia/ocab118.CrossRef

73.

Maurer LR, et al. Trauma outcome predictor: an artificial intelligence interactive smartphone tool to predict outcomes in trauma patients. J Trauma Acute Care Surg. 2021;91(1):93–9. https://doi.org/10.1097/TA.0000000000003158.CrossRefPubMed

74.

Puttinaovarat S, Pruitikanee S, Kongcharoen J, Horkaew P. Machine learning based emergency patient classification system. Int J Online Biomed Eng. 2021;17(05):133. https://doi.org/10.3991/ijoe.v17i05.22341.CrossRef

75.

Yun H, Choi J, Park JH. Prediction of critical care outcome for adult patients presenting to emergency department using initial triage information: an XGBoost algorithm analysis. JMIR Med Inform. 2021;9(9):e30770. https://doi.org/10.2196/30770.CrossRefPubMedPubMedCentral

76.

Hwang S, Lee B. Machine learning-based prediction of critical illness in children visiting the emergency department. PLoS ONE. 2022;17(2):e0264184. https://doi.org/10.1371/journal.pone.0264184.CrossRefPubMedPubMedCentral

77.

Lee S, Kang WS, Seo S, Kim DW, Ko H. Model for predicting in-hospital mortality of physical trauma patients using artificial intelligence techniques: nationwide population-based study in Korea. J Med Internet Res. 2022;24(12):1–18. https://doi.org/10.2196/43757.CrossRef

78.

Xie F, et al. Benchmarking emergency department prediction models with machine learning and public electronic health records. Sci Data. 2022;9(1):658. https://doi.org/10.1038/s41597-022-01782-9.CrossRefPubMedPubMedCentral

79.

Cotte F, et al. Safety of triage self-assessment using a symptom assessment app for walk-in patients in the emergency care setting: observational prospective cross-sectional study. JMIR mHealth uHealth. 2022;10(3):e32340. https://doi.org/10.2196/32340.CrossRefPubMedPubMedCentral

80.

Vântu A, Vasilescu A, Băicoianu A. Medical emergency department triage data processing using a machine-learning solution. Heliyon. 2023;9(8):e18402. https://doi.org/10.1016/j.heliyon.2023.e18402.CrossRefPubMedPubMedCentral

81.

A. Ahmed, M. Al-Maamari, M. Firouz, and D. Delen, “An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations,” Inf. Syst. Front., no. 0123456789, Sep. 2023, https://doi.org/10.1007/s10796-023-10431-4.

82.

Lee S, et al. An artificial intelligence model for predicting trauma mortality among emergency department patients in South Korea: retrospective cohort study. J Med Internet Res. 2023;25:e49283. https://doi.org/10.2196/49283.CrossRefPubMedPubMedCentral

83.

Choi A, et al. Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department. Sci Rep. 2023;13(1):8561. https://doi.org/10.1038/s41598-023-35617-3.CrossRefPubMedPubMedCentral

84.

Chang H, et al. Clinical support system for triage based on federated learning for the Korea triage and acuity scale. Heliyon. 2023;9(8):e19210. https://doi.org/10.1016/j.heliyon.2023.e19210.CrossRefPubMedPubMedCentral

85.

Chen Y-HJ, et al. An AI-enabled dynamic risk stratification for emergency department patients with ECG and CXR integration. J Med Syst. 2023;47(1):81. https://doi.org/10.1007/s10916-023-01980-x.CrossRefPubMed

86.

Hall JN, Galaev R, Gavrilov M, Mondoux S. Development of a machine learning-based acuity score prediction model for virtual care settings. BMC Med Inform Decis Mak. 2023;23(1):200. https://doi.org/10.1186/s12911-023-02307-z.CrossRefPubMedPubMedCentral

87.

D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, Applied Logistic Regression, vol. 47, no. 4. in Wiley Series in Probability and Statistics, vol. 47. Wiley, 2013. https://doi.org/10.1002/9781118548387.

88.

Moons KGM, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med. 2019;170(1):W1. https://doi.org/10.7326/M18-1377.CrossRefPubMed

89.

Biswas SS. Role of Chat GPT in Public health. Ann Biomed Eng. 2023;51(5):868–9. https://doi.org/10.1007/s10439-023-03172-7.CrossRefPubMed

90.

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57. https://doi.org/10.1613/jair.953.CrossRef

91.

J. Li et al., “Feature Selection: A Data Perspective,” ACM Comput. Surv., vol. 50, no. 6, Jan. 2016, https://doi.org/10.1145/3136625.

92.

A. Madevska Bogdanova, B. Koteska, T. Vićentić, S. D. Ilić, M. Tomić, and M. Spasenović, “Blood Oxygen Saturation Estimation with Laser-Induced Graphene Respiration Sensor,” J. Sensors, vol. 2024, pp. 1–10, Jan. 2024, https://doi.org/10.1155/2024/4696031.

93.

Chen Y, et al. Machine learning model identification and prediction of patients’ need for icu admission: a systematic review. Am J Emerg Med. 2023;73:166–70. https://doi.org/10.1016/j.ajem.2023.08.043.CrossRefPubMed

94.

Razo C, et al. Effects of elevated systolic blood pressure on ischemic heart disease: a burden of proof study. Nat Med. 2022;28(10):2056–65. https://doi.org/10.1038/s41591-022-01974-1.CrossRefPubMedPubMedCentral

95.

M. Kuhn and K. Johnson, “3.4 Resampling,” in Feature Engineering and Selection: A Practical Approach for Predictive Models, Taylor & Francis Group, 2019. Available: https://bookdown.org/max/FES/resampling.html#rolling-origin-forecasting

96.

T. Verdonck, B. Baesens, M. Óskarsdóttir, and S. vanden Broucke, “Special issue on feature engineering editorial,” Mach. Learn., no. 0123456789, Aug. 2021, https://doi.org/10.1007/s10994-021-06042-2.

Title: Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review
Author: Bruno Matos Porto
Publication date: 01-12-2024
Publisher: BioMed Central
Keywords: Triage
Artificial Intelligence
Published in: BMC Emergency Medicine / Issue 1/2024
Electronic ISSN: 1471-227X
DOI: https://doi.org/10.1186/s12873-024-01135-2

Can ketamine administration prevent intubation in patients who cannot comply with NIV due to agitation?

Open Access
Ketamine
Research

Nurses’ challenges for disaster response: a qualitative study

Open Access
Research

Take-home naloxone administered in emergency settings: feasibility of intervention implementation in a cluster randomized trial

Open Access
Naloxone
Research

Effects of an advanced first aid course or real-time video communication with ambulance personnel on layperson first response for building-site severe injury events: a simulation study

Open Access
Telemedicine
Research

Is alcohol and psychoactive medication use associated with excess hospital length-of-stay and admission frequency? A cross-sectional, observational study

Open Access
Benzodiazepine
Research

Redesign of a virtual reality basic life support module for medical training – a feasibility study

Open Access
Cardiopulmonary Resuscitation
Research

Keynote series | Spotlight on menopause

Springer Medicine

Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review

Abstract

Background

Methods

Results

Conclusion

Other articles of this Issue 1/2024

Can ketamine administration prevent intubation in patients who cannot comply with NIV due to agitation?

Nurses’ challenges for disaster response: a qualitative study

Take-home naloxone administered in emergency settings: feasibility of intervention implementation in a cluster randomized trial

Effects of an advanced first aid course or real-time video communication with ambulance personnel on layperson first response for building-site severe injury events: a simulation study

Is alcohol and psychoactive medication use associated with excess hospital length-of-stay and admission frequency? A cross-sectional, observational study

Redesign of a virtual reality basic life support module for medical training – a feasibility study

Keynote series | Spotlight on menopause

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

Please log in to get access to this content

Can ketamine administration prevent intubation in patients who cannot comply with NIV due to agitation?

Nurses’ challenges for disaster response: a qualitative study

Take-home naloxone administered in emergency settings: feasibility of intervention implementation in a cluster randomized trial

Effects of an advanced first aid course or real-time video communication with ambulance personnel on layperson first response for building-site severe injury events: a simulation study

Is alcohol and psychoactive medication use associated with excess hospital length-of-stay and admission frequency? A cross-sectional, observational study

Redesign of a virtual reality basic life support module for medical training – a feasibility study