Application status and prospect of artificial intelligence in emergency medicine_West China Medical Journal

Authors：

YAO Peng ^1,2 , TANG Shiyuan ^1,2 , JIANG Yaowen ^1,2 ,  CAO Yu ^1,2

1. Department of Emergency Medicine, Laboratory of Emergency Medicine, West China School of Medicine / West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Disaster Medical Center, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

CAO Yu, Email: yuyuer@126.com

Keywords：

Emergency medicine; artificial intelligence; machine learning; deep learning

DOI：

10.7507/1002-0179.202211049

Video：

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With the innovation and breakthrough of key technologies in smart medicine, actively exploring smart emergency measures and methods with artificial intelligence as the core technology is helpful to improve the ability of emergency medical team to diagnose and treat acute and critical diseases. This paper reviews the application status of artificial intelligence in pre-hospital and in-hospital diagnosis and treatment capabilities and system construction, expounds on the challenges it faces and possible coping strategies, and provides a reference for the in-depth integration and development of “artificial intelligence + emergency medicine” education, research and production during the new wave of scientific and technological revolution.

Citation： YAO Peng, TANG Shiyuan, JIANG Yaowen, CAO Yu. Application status and prospect of artificial intelligence in emergency medicine. West China Medical Journal, 2022, 37(11): 1601-1606. doi: 10.7507/1002-0179.202211049 Copy

1.	Yin J, Ngiam KY, Teo HH. Role of artificial intelligence applications in real-life clinical practice: systematic review. J Med Internet Res, 2021, 23(4): e25759.
2.	Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng, 2018, 2(10): 719-731.
3.	Lindsey R, Daluiski A, Chopra S, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A, 2018, 115(45): 11591-11596.
4.	Nas S, Koyuncu M. Emergency department capacity planning: a recurrent neural network and simulation approach. Comput Math Methods Med, 2019, 2019: 4359719.
5.	Shafaf N, Malek H. Applications of machine learning approaches in emergency medicine; a review article. Arch Acad Emerg Med, 2019, 7(1): 34.
6.	葛芳民, 李强, 林高兴, 等. 基于 5G 技术院前-院内急诊医疗服务平台建设的研究. 中华急诊医学杂志, 2019, 28(10): 1223-1227.
7.	Zhang Z, Zhou D, Zhang J, et al. Multilayer perceptron-based prediction of stroke mimics in prehospital triage. Sci Rep, 2022, 12(1): 17994.
8.	Mayampurath A, Parnianpour Z, Richards CT, et al. Improving prehospital stroke diagnosis using natural language processing of paramedic reports. Stroke, 2021, 52(8): 2676-2679.
9.	Hayashi Y, Shimada T, Hattori N, et al. A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study. Sci Rep, 2021, 11(1): 20519.
10.	Chen KW, Wang YC, Liu MH, et al. Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care. Front Cardiovasc Med, 2022, 9: 1001982.
11.	Chin KC, Cheng YC, Sun JT, et al. Machine learning-based text analysis to predict severely injured patients in emergency medical dispatch: model development and validation. J Med Internet Res, 2022, 24(6): e30210.
12.	Duceau B, Alsac JM, Bellenfant F, et al. Prehospital triage of acute aortic syndrome using a machine learning algorithm. Br J Surg, 2020, 107(8): 995-1003.
13.	Prieto JT, Scott K, McEwen D, et al. The detection of opioid misuse and heroin use from paramedic response documentation: machine learning for improved surveillance. J Med Internet Res, 2020, 22(1): e15645.
14.	Sun BC, Hsia RY, Weiss RE, et al. Effect of emergency department crowding on outcomes of admitted patients. Ann Emerg Med, 2013, 61(6): 605-611.e6.
15.	Gaieski DF, Agarwal AK, Mikkelsen ME, et al. The impact of ED crowding on early interventions and mortality in patients with severe sepsis. Am J Emerg Med, 2017, 35(7): 953-960.
16.	Raita Y, Goto T, Faridi MK, et al. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care, 2019, 23(1): 64.
17.	Mistry B, Stewart De Ramirez S, Kelen G, 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.
18.	Goto T, Camargo CA Jr, Faridi MK, et al. Machine learning-based prediction of clinical outcomes for children during emergency department triage. JAMA Netw Open, 2019, 2(1): e186937.
19.	Yu JY, Xie F, Nan L, et al. An external validation study of the Score For Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department. Sci Rep, 2022, 12(1): 17466.
20.	Chen TL, Chen JC, Chang WH, et al. Imbalanced prediction of emergency department admission using natural language processing and deep neural network. J Biomed Inform, 2022, 133: 104171.
21.	Burdick H, Pino E, Gabel-Comeau D, et al. Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals. BMC Med Inform Decis Mak, 2020, 20(1): 276.
22.	Henry KE, Adams R, Parent C, et al. Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing. Nat Med, 2022, 28(7): 1447-1454.
23.	Fleuren LM, Klausch TLT, Zwager CL, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med, 2020, 46(3): 383-400.
24.	Komorowski M. Clinical management of sepsis can be improved by artificial intelligence: yes. Intensive Care Med, 2020, 46(2): 375-377.
25.	Emakhu J, Monplaisir L, Aguwa C, et al. Acute coronary syndrome prediction in emergency care: a machine learning approach. Comput Methods Programs Biomed, 2022, 225: 107080.
26.	Yadgir SR, Engstrom C, Jacobsohn GC, et al. Machine learning-assisted screening for cognitive impairment in the emergency department. J Am Geriatr Soc, 2022, 70(3): 831-837.
27.	Iacobucci G. Covid-19: MHRA is concerned over use of rapid lateral flow devices for mass testing. BMJ, 2021, 373: n1090.
28.	Dinnes J, Deeks JJ, Berhane S, et al. Rapid, point-of-care antigen and molecular-based tests for diagnosis of SARS-CoV-2 infection. Cochrane Database Syst Rev, 2021, 3(3): CD013705.
29.	Soltan AAS, Yang J, Pattanshetty R, et al. Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening. Lancet Digit Health, 2022, 4(4): e266-e278.
30.	Ng DL, Granados AC, Santos YA, et al. A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood. Sci Adv, 2021, 7(6): eabe5984.
31.	Vearrier L, Derse AR, Basford JB, et al. Artificial intelligence in emergency medicine: benefits, risks, and recommendations. J Emerg Med, 2022, 62(4): 492-499.
32.	Guly HR. Diagnostic errors in an accident and emergency department. Emerg Med J, 2001, 18(4): 263-269.
33.	Duron L, Ducarouge A, Gillibert A, et al. Assessment of an AI aid in detection of adult appendicular skeletal fractures by emergency physicians and radiologists: a multicenter cross-sectional diagnostic study. Radiology, 2021, 300(1): 120-129.
34.	Guermazi A, Tannoury C, Kompel AJ, et al. Improving radiographic fracture recognition performance and efficiency using artificial intelligence. Radiology, 2022, 302(3): 627-636.
35.	Hwang EJ, Nam JG, Lim WH, et al. Deep learning for chest radiograph diagnosis in the emergency department. Radiology, 2019, 293(3): 573-580.
36.	Berlyand Y, Raja AS, Dorner SC, et al. How artificial intelligence could transform emergency department operations. Am J Emerg Med, 2018, 36(8): 1515-1517.
37.	Seymour CW, Kennedy JN, Wang S, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA, 2019, 321(20): 2003-2017.
38.	Maddali MV, Churpek M, Pham T, et al. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis. Lancet Respir Med, 2022, 10(4): 367-377.
39.	Poncette AS, Mosch L, Spies C, et al. Improvements in patient monitoring in the intensive care unit: survey study. J Med Internet Res, 2020, 22(6): e19091.
40.	Angehrn Z, Haldna L, Zandvliet AS, et al. Artificial intelligence and machine learning applied at the point of care. Front Pharmacol, 2020, 11: 759.
41.	Zhang J, Zhou F, Qi H, et al. Randomized study of individualized pharmacokinetically-guided dosing of paclitaxel compared with body-surface area dosing in Chinese patients with advanced non-small cell lung cancer. Br J Clin Pharmacol, 2019, 85(10): 2292-2301.
42.	Kareemi H, Vaillancourt C, Rosenberg H, et al. Machine learning versus usual care for diagnostic and prognostic prediction in the emergency department: a systematic review. Acad Emerg Med, 2021, 28(2): 184-196.
43.	Chen YM, Kao Y, Hsu CC, et al. Real-time interactive artificial intelligence of things-based prediction for adverse outcomes in adult patients with pneumonia in the emergency department. Acad Emerg Med, 2021, 28(11): 1277-1285.
44.	Wardi G, Carlile M, Holder A, et al. Predicting progression to septic shock in the emergency department using an externally generalizable machine-learning algorithm. Ann Emerg Med, 2021, 77(4): 395-406.
45.	Sax DR, Mark DG, Huang J, et al. Use of machine learning to develop a risk-stratification tool for emergency department patients with acute heart failure. Ann Emerg Med, 2021, 77(2): 237-248.
46.	Ziobrowski HN, Kennedy CJ, Ustun B, et al. Development and validation of a model to predict posttraumatic stress disorder and major depression after a motor vehicle collision. JAMA Psychiatry, 2021, 78(11): 1228-1237.
47.	Qi W, Gevonden M, Shalev A. Prevention of post-traumatic stress disorder after trauma: current evidence and future directions. Curr Psychiatry Rep, 2016, 18(2): 20.
48.	Atwoli L, Stein DJ, Koenen KC, et al. Epidemiology of posttraumatic stress disorder: prevalence, correlates and consequences. Curr Opin Psychiatry, 2015, 28(4): 307-311.
49.	Koenen KC, Ratanatharathorn A, Ng L, et al. Posttraumatic stress disorder in the world mental health surveys. Psychol Med, 2017, 47(13): 2260-2274.
50.	Heldt FS, Vizcaychipi MP, Peacock S, et al. Early risk assessment for COVID-19 patients from emergency department data using machine learning. Sci Rep, 2021, 11(1): 4200.
51.	Duckworth C, Chmiel FP, Burns DK, et al. Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19. Sci Rep, 2021, 11(1): 23017.
52.	Hu C, Liu Z, Jiang Y, et al. Early prediction of mortality risk among patients with severe COVID-19, using machine learning. Int J Epidemiol, 2021, 49(6): 1918-1929.
53.	Coburn B, Morris AM, Tomlinson G, et al. Does this adult patient with suspected bacteremia require blood cultures?. JAMA, 2012, 308(5): 502-511.
54.	Long B, Koyfman A. Best clinical practice: blood culture utility in the emergency department. J Emerg Med, 2016, 51(5): 529-539.
55.	Mountain D, Bailey PM, O’Brien D, et al. Blood cultures ordered in the adult emergency department are rarely useful. Eur J Emerg Med, 2006, 13(2): 76-79.
56.	Schinkel M, Boerman AW, Bennis FC, et al. Diagnostic stewardship for blood cultures in the emergency department: a multicenter validation and prospective evaluation of a machine learning prediction tool. EBioMedicine, 2022, 82: 104176.
57.	Patel SJ, Chamberlain DB, Chamberlain JM. A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage. Acad Emerg Med, 2018, 25(12): 1463-1470.
58.	Fenn A, Davis C, Buckland DM, et al. Development and validation of machine learning models to predict admission from emergency department to inpatient and intensive care units. Ann Emerg Med, 2021, 78(2): 290-302.
59.	Mišić VV, Gabel E, Hofer I, et al. Machine learning prediction of postoperative emergency department hospital readmission. Anesthesiology, 2020, 132(5): 968-980.
60.	Shung DL, Au B, Taylor RA, et al. Validation of a machine learning model that outperforms clinical risk scoring systems for upper gastrointestinal bleeding. Gastroenterology, 2020, 158(1): 160-167.
61.	Kamran F, Tang S, Otles E, et al. Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study. BMJ, 2022, 376: e068576.
62.	Shamout FE, Shen Y, Wu N, et al. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. NPJ Digit Med, 2021, 4(1): 80.
63.	He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med, 2019, 25(1): 30-36.
64.	Panch T, Mattie H, Celi LA. The “inconvenient truth” about AI in healthcare. NPJ Digit Med, 2019, 2: 77.
65.	Kelly CJ, Karthikesalingam A, Suleyman M, et al. Key challenges for delivering clinical impact with artificial intelligence. BMC Med, 2019, 17(1): 195.
66.	Garnacho-Montero J, Martín-Loeches I. Clinical management of sepsis can be improved by artificial intelligence: no. Intensive Care Med, 2020, 46(2): 378-380.
67.	Price WN 2nd, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. JAMA, 2019, 322(18): 1765-1766.
68.	Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 2017, 69S: S36-S40.

1. Yin J, Ngiam KY, Teo HH. Role of artificial intelligence applications in real-life clinical practice: systematic review. J Med Internet Res, 2021, 23(4): e25759.
2. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng, 2018, 2(10): 719-731.
3. Lindsey R, Daluiski A, Chopra S, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A, 2018, 115(45): 11591-11596.
4. Nas S, Koyuncu M. Emergency department capacity planning: a recurrent neural network and simulation approach. Comput Math Methods Med, 2019, 2019: 4359719.
5. Shafaf N, Malek H. Applications of machine learning approaches in emergency medicine; a review article. Arch Acad Emerg Med, 2019, 7(1): 34.
6. 葛芳民, 李强, 林高兴, 等. 基于 5G 技术院前-院内急诊医疗服务平台建设的研究. 中华急诊医学杂志, 2019, 28(10): 1223-1227.
7. Zhang Z, Zhou D, Zhang J, et al. Multilayer perceptron-based prediction of stroke mimics in prehospital triage. Sci Rep, 2022, 12(1): 17994.
8. Mayampurath A, Parnianpour Z, Richards CT, et al. Improving prehospital stroke diagnosis using natural language processing of paramedic reports. Stroke, 2021, 52(8): 2676-2679.
9. Hayashi Y, Shimada T, Hattori N, et al. A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study. Sci Rep, 2021, 11(1): 20519.
10. Chen KW, Wang YC, Liu MH, et al. Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care. Front Cardiovasc Med, 2022, 9: 1001982.
11. Chin KC, Cheng YC, Sun JT, et al. Machine learning-based text analysis to predict severely injured patients in emergency medical dispatch: model development and validation. J Med Internet Res, 2022, 24(6): e30210.
12. Duceau B, Alsac JM, Bellenfant F, et al. Prehospital triage of acute aortic syndrome using a machine learning algorithm. Br J Surg, 2020, 107(8): 995-1003.
13. Prieto JT, Scott K, McEwen D, et al. The detection of opioid misuse and heroin use from paramedic response documentation: machine learning for improved surveillance. J Med Internet Res, 2020, 22(1): e15645.
14. Sun BC, Hsia RY, Weiss RE, et al. Effect of emergency department crowding on outcomes of admitted patients. Ann Emerg Med, 2013, 61(6): 605-611.e6.
15. Gaieski DF, Agarwal AK, Mikkelsen ME, et al. The impact of ED crowding on early interventions and mortality in patients with severe sepsis. Am J Emerg Med, 2017, 35(7): 953-960.
16. Raita Y, Goto T, Faridi MK, et al. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care, 2019, 23(1): 64.
17. Mistry B, Stewart De Ramirez S, Kelen G, 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.
18. Goto T, Camargo CA Jr, Faridi MK, et al. Machine learning-based prediction of clinical outcomes for children during emergency department triage. JAMA Netw Open, 2019, 2(1): e186937.
19. Yu JY, Xie F, Nan L, et al. An external validation study of the Score For Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department. Sci Rep, 2022, 12(1): 17466.
20. Chen TL, Chen JC, Chang WH, et al. Imbalanced prediction of emergency department admission using natural language processing and deep neural network. J Biomed Inform, 2022, 133: 104171.
21. Burdick H, Pino E, Gabel-Comeau D, et al. Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals. BMC Med Inform Decis Mak, 2020, 20(1): 276.
22. Henry KE, Adams R, Parent C, et al. Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing. Nat Med, 2022, 28(7): 1447-1454.
23. Fleuren LM, Klausch TLT, Zwager CL, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med, 2020, 46(3): 383-400.
24. Komorowski M. Clinical management of sepsis can be improved by artificial intelligence: yes. Intensive Care Med, 2020, 46(2): 375-377.
25. Emakhu J, Monplaisir L, Aguwa C, et al. Acute coronary syndrome prediction in emergency care: a machine learning approach. Comput Methods Programs Biomed, 2022, 225: 107080.
26. Yadgir SR, Engstrom C, Jacobsohn GC, et al. Machine learning-assisted screening for cognitive impairment in the emergency department. J Am Geriatr Soc, 2022, 70(3): 831-837.
27. Iacobucci G. Covid-19: MHRA is concerned over use of rapid lateral flow devices for mass testing. BMJ, 2021, 373: n1090.
28. Dinnes J, Deeks JJ, Berhane S, et al. Rapid, point-of-care antigen and molecular-based tests for diagnosis of SARS-CoV-2 infection. Cochrane Database Syst Rev, 2021, 3(3): CD013705.
29. Soltan AAS, Yang J, Pattanshetty R, et al. Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening. Lancet Digit Health, 2022, 4(4): e266-e278.
30. Ng DL, Granados AC, Santos YA, et al. A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood. Sci Adv, 2021, 7(6): eabe5984.
31. Vearrier L, Derse AR, Basford JB, et al. Artificial intelligence in emergency medicine: benefits, risks, and recommendations. J Emerg Med, 2022, 62(4): 492-499.
32. Guly HR. Diagnostic errors in an accident and emergency department. Emerg Med J, 2001, 18(4): 263-269.
33. Duron L, Ducarouge A, Gillibert A, et al. Assessment of an AI aid in detection of adult appendicular skeletal fractures by emergency physicians and radiologists: a multicenter cross-sectional diagnostic study. Radiology, 2021, 300(1): 120-129.
34. Guermazi A, Tannoury C, Kompel AJ, et al. Improving radiographic fracture recognition performance and efficiency using artificial intelligence. Radiology, 2022, 302(3): 627-636.
35. Hwang EJ, Nam JG, Lim WH, et al. Deep learning for chest radiograph diagnosis in the emergency department. Radiology, 2019, 293(3): 573-580.
36. Berlyand Y, Raja AS, Dorner SC, et al. How artificial intelligence could transform emergency department operations. Am J Emerg Med, 2018, 36(8): 1515-1517.
37. Seymour CW, Kennedy JN, Wang S, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA, 2019, 321(20): 2003-2017.
38. Maddali MV, Churpek M, Pham T, et al. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis. Lancet Respir Med, 2022, 10(4): 367-377.
39. Poncette AS, Mosch L, Spies C, et al. Improvements in patient monitoring in the intensive care unit: survey study. J Med Internet Res, 2020, 22(6): e19091.
40. Angehrn Z, Haldna L, Zandvliet AS, et al. Artificial intelligence and machine learning applied at the point of care. Front Pharmacol, 2020, 11: 759.
41. Zhang J, Zhou F, Qi H, et al. Randomized study of individualized pharmacokinetically-guided dosing of paclitaxel compared with body-surface area dosing in Chinese patients with advanced non-small cell lung cancer. Br J Clin Pharmacol, 2019, 85(10): 2292-2301.
42. Kareemi H, Vaillancourt C, Rosenberg H, et al. Machine learning versus usual care for diagnostic and prognostic prediction in the emergency department: a systematic review. Acad Emerg Med, 2021, 28(2): 184-196.
43. Chen YM, Kao Y, Hsu CC, et al. Real-time interactive artificial intelligence of things-based prediction for adverse outcomes in adult patients with pneumonia in the emergency department. Acad Emerg Med, 2021, 28(11): 1277-1285.
44. Wardi G, Carlile M, Holder A, et al. Predicting progression to septic shock in the emergency department using an externally generalizable machine-learning algorithm. Ann Emerg Med, 2021, 77(4): 395-406.
45. Sax DR, Mark DG, Huang J, et al. Use of machine learning to develop a risk-stratification tool for emergency department patients with acute heart failure. Ann Emerg Med, 2021, 77(2): 237-248.
46. Ziobrowski HN, Kennedy CJ, Ustun B, et al. Development and validation of a model to predict posttraumatic stress disorder and major depression after a motor vehicle collision. JAMA Psychiatry, 2021, 78(11): 1228-1237.
47. Qi W, Gevonden M, Shalev A. Prevention of post-traumatic stress disorder after trauma: current evidence and future directions. Curr Psychiatry Rep, 2016, 18(2): 20.
48. Atwoli L, Stein DJ, Koenen KC, et al. Epidemiology of posttraumatic stress disorder: prevalence, correlates and consequences. Curr Opin Psychiatry, 2015, 28(4): 307-311.
49. Koenen KC, Ratanatharathorn A, Ng L, et al. Posttraumatic stress disorder in the world mental health surveys. Psychol Med, 2017, 47(13): 2260-2274.
50. Heldt FS, Vizcaychipi MP, Peacock S, et al. Early risk assessment for COVID-19 patients from emergency department data using machine learning. Sci Rep, 2021, 11(1): 4200.
51. Duckworth C, Chmiel FP, Burns DK, et al. Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19. Sci Rep, 2021, 11(1): 23017.
52. Hu C, Liu Z, Jiang Y, et al. Early prediction of mortality risk among patients with severe COVID-19, using machine learning. Int J Epidemiol, 2021, 49(6): 1918-1929.
53. Coburn B, Morris AM, Tomlinson G, et al. Does this adult patient with suspected bacteremia require blood cultures?. JAMA, 2012, 308(5): 502-511.
54. Long B, Koyfman A. Best clinical practice: blood culture utility in the emergency department. J Emerg Med, 2016, 51(5): 529-539.
55. Mountain D, Bailey PM, O’Brien D, et al. Blood cultures ordered in the adult emergency department are rarely useful. Eur J Emerg Med, 2006, 13(2): 76-79.
56. Schinkel M, Boerman AW, Bennis FC, et al. Diagnostic stewardship for blood cultures in the emergency department: a multicenter validation and prospective evaluation of a machine learning prediction tool. EBioMedicine, 2022, 82: 104176.
57. Patel SJ, Chamberlain DB, Chamberlain JM. A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage. Acad Emerg Med, 2018, 25(12): 1463-1470.
58. Fenn A, Davis C, Buckland DM, et al. Development and validation of machine learning models to predict admission from emergency department to inpatient and intensive care units. Ann Emerg Med, 2021, 78(2): 290-302.
59. Mišić VV, Gabel E, Hofer I, et al. Machine learning prediction of postoperative emergency department hospital readmission. Anesthesiology, 2020, 132(5): 968-980.
60. Shung DL, Au B, Taylor RA, et al. Validation of a machine learning model that outperforms clinical risk scoring systems for upper gastrointestinal bleeding. Gastroenterology, 2020, 158(1): 160-167.
61. Kamran F, Tang S, Otles E, et al. Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study. BMJ, 2022, 376: e068576.
62. Shamout FE, Shen Y, Wu N, et al. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. NPJ Digit Med, 2021, 4(1): 80.
63. He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med, 2019, 25(1): 30-36.
64. Panch T, Mattie H, Celi LA. The “inconvenient truth” about AI in healthcare. NPJ Digit Med, 2019, 2: 77.
65. Kelly CJ, Karthikesalingam A, Suleyman M, et al. Key challenges for delivering clinical impact with artificial intelligence. BMC Med, 2019, 17(1): 195.
66. Garnacho-Montero J, Martín-Loeches I. Clinical management of sepsis can be improved by artificial intelligence: no. Intensive Care Med, 2020, 46(2): 378-380.
67. Price WN 2nd, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. JAMA, 2019, 322(18): 1765-1766.
68. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 2017, 69S: S36-S40.

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