Artificial intelligence and machine learning in emergency medicine: a narrative review

Brianna Mueller, Takahiro Kinoshita, Alexander Peebles, Mark A Graber, Sangil Lee, Brianna Mueller, Takahiro Kinoshita, Alexander Peebles, Mark A Graber, Sangil Lee

Abstract

Aim: The emergence and evolution of artificial intelligence (AI) has generated increasing interest in machine learning applications for health care. Specifically, researchers are grasping the potential of machine learning solutions to enhance the quality of care in emergency medicine.

Methods: We undertook a narrative review of published works on machine learning applications in emergency medicine and provide a synopsis of recent developments.

Results: This review describes fundamental concepts of machine learning and presents clinical applications for triage, risk stratification specific to disease, medical imaging, and emergency department operations. Additionally, we consider how machine learning models could contribute to the improvement of causal inference in medicine, and to conclude, we discuss barriers to safe implementation of AI.

Conclusion: We intend that this review serves as an introduction to AI and machine learning in emergency medicine.

Keywords: Artificial intelligence; deep learning; emergency medicine; machine learning; prediction.

© 2022 The Authors. Acute Medicine & Surgery published by John Wiley & Sons Australia, Ltd on behalf of Japanese Association for Acute Medicine.

Figures

Fig. 1
Fig. 1
Artificial neural network, the basis of deep learning algorithms.
Fig. 2
Fig. 2
Classification and regression tree to predict medication dosage. BMI, body mass index; PMH, previous medical history.

References

    1. Lee S, Mohr NM, Street WN, Nadkarni P. Machine learning in relation to emergency medicine clinical and operational scenarios: an overview. West J. Emerg. Med. 2019; 20: 219–27.
    1. Helm JM, Swiergosz AM, Haeberle HS et al. Machine learning and artificial intelligence: definitions, applications, and future directions. Curr. Rev. Musculoskelet Med. 2020;13:69‐76.
    1. Esteva A, Robicquet A, Ramsundar B et al. A guide to deep learning in healthcare. Nat. Med. 2019;25:24‐9.
    1. Mzoughi H, Njeh I, Wali A et al. Deep Multi‐Scale 3D Convolutional Neural Network (CNN) for MRI gliomas brain tumor classification. J. Digit. Imaging 2020;33:903‐15.
    1. Khan AI, Shah JL, Bhat MM. CoroNet: a deep neural network for detection and diagnosis of COVID‐19 from chest x‐ray images. Comput. Methods Programs Biomed. 2020; 196: 105581.
    1. Qummar S, Khan FG, Shah S et al. A deep learning ensemble approach for diabetic retinopathy detection. IEEE Access. 2019;7:150530‐9.
    1. Young T, Hazarika D, Poria S, Cambria E. Recent trends in deep learning based natural language processing [review article]. IEEE Comput. Intell. Mag. 2018; 13: 55–75.
    1. Wunnava S, Qin X, Kakar T, Sen C, Rundensteiner EA, Kong X. Adverse drug event detection from electronic health records using hierarchical recurrent neural networks with dual‐level embedding. Drug Saf. 2019; 42: 113–22.
    1. Wu S, Roberts K, Datta S et al. Deep learning in clinical natural language processing: a methodical review. J. Am. Med. Inform. Assoc. 2020;27:457‐70.
    1. Kaufman DR, Sheehan B, Stetson P et al. Natural language processing‐enabled and conventional data capture methods for input to electronic health records: a comparative usability study. JMIR Med. Inform. 2016;4:e35.
    1. Rink B, Roberts K, Harabagiu S et al. Extracting actionable findings of appendicitis from radiology reports using natural language processing. AMIA Jt. Summits Transl. Sci. Proc. 2013;2013:221.
    1. Doan S, Maehara CK, Chaparro JD et al. Building a natural language processing tool to identify patients with high clinical suspicion for Kawasaki disease from emergency department notes. Acad. Emerg. Med. 2016;23:628‐36.
    1. Ferraro JP, Ye Y, Gesteland PH, et al. The effects of natural language processing on cross‐institutional portability of influenza case detection for disease surveillance. Appl. Clin. Inform. 2017;8:560‐80.
    1. Arya R, Wei G, McCoy JV, Crane J, Ohman‐Strickland P, Eisenstein RM. Decreasing length of stay in the emergency department with a split emergency severity index 3 patient flow model. Acad. Emerg. Med. 2013; 20: 1171–9.
    1. Chilamkurthy S, Ghosh R, Tanamala S et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018;392:2388‐96.
    1. Ginat DT. Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage. Neuroradiology 2020; 62(3): 335–40. 10.1007/s00234-019-02330-w.
    1. Rao B, Zohrabian V, Cedeno P, Saha A, Pahade J, Davis MA. Utility of artificial intelligence tool as a prospective radiology peer reviewer ‐ detection of unreported intracranial hemorrhage. Acad. Radiol. 2021; 28: 85–93.
    1. Schaffter T, Buist DSM, Lee CI et al. Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw. Open. 2020;3:e200265.
    1. Berlyand Y, Raja AS, Dorner SC et al. How artificial intelligence could transform emergency department operations. Am. J. Emerg. Med. 2018;36:1515‐7.
    1. Jilani T, Housley G, Figueredo G, Tang P‐S, Hatton J, Shaw D. Short and Long term predictions of Hospital emergency department attendances. Int. J. Med. Inform. 2019; 129: 167–74.
    1. 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: 64.
    1. Ivanov O, Wolf L, Brecher D et al. Improving ED emergency severity index acuity assignment using machine learning and clinical natural language processing. J. Emerg. Nurs.. 2021;47:265‐78. e7.
    1. 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: 104146.
    1. Obeid JS, Weeda ER, Matuskowitz AJ et al. Automated detection of altered mental status in emergency department clinical notes: a deep learning approach. BMC Med. Inform. Decis. Mak. 2019;19:164.
    1. 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: 1463–70.
    1. Klang E, Kummer BR, Dangayach NS 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;11:1381.
    1. Kim J, Chang H, Kim D, Jang D‐H, Park I, Kim K. Machine learning for prediction of septic shock at initial triage in emergency department. J. Crit. Care. 2020; 55: 163–70.
    1. Taylor RA, Moore CL, Cheung K‐H, Brandt C. Predicting urinary tract infections in the emergency department with machine learning. PLoS One 2018; 13: e0194085.
    1. Lindsey R, Daluiski A, Chopra S et al. Deep neural network improves fracture detection by clinicians. Proc. Natl. Acad. Sci. U. S. A. 2018;115:11591‐6.
    1. Feng M, McSparron JI, Kien DT et al. Transthoracic echocardiography and mortality in sepsis: analysis of the MIMIC‐III database. Intens. Care Med. 2018; 44: 884–92.
    1. Pak A, Gannon B, Staib A. Predicting waiting time to treatment for emergency department patients. Int. J. Med. Inform. 2021; 145: 104303.
    1. Lee S, Lee YH. Improving emergency department efficiency by patient scheduling using deep reinforcement learning. Healthcare (Basel) 2020; 8: 77.
    1. Xu M, Wong TC, Chin KS. A medical procedure‐based patient grouping method for an emergency department. Appl. Soft. Comput. 2014; 14: 31–7.
    1. Hernán MA, Hsu J, Healy B. A second chance to get causal inference right: A classification of data science tasks. CHANCE 2019; 32: 42–9.
    1. Seymour CW, Kennedy JN, Wang S et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA 2019;321:2003‐17.
    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: e25759.
    1. Ahmed S, Nutt CT, Eneanya ND et al. Examining the potential impact of race multiplier utilization in estimated glomerular filtration rate calculation on African‐American care outcomes. J. Gen. Intern. Med. 2021;36:464‐71.
    1. A drug addiction risk algorithm and its grim toll on chronic pain sufferers | WIRED. Accessed 23 Nov 2021.
    1. Guo A, Kamar E, Vaughan JW, Wallach H, Morris MR. Toward fairness in AI for people with disabilities SBG@a research roadmap. SIGACCESS Access Comput. 2020; 125: 1–1.
    1. Soares WE, Knee A, Gemme SR et al. A prospective evaluation of clinical HEART score agreement, accuracy, and adherence in emergency department chest pain patients. Ann. Emerg. Med. 2021;78:231‐41.
    1. Wong A, Otles E, Donnelly JP et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern. Med. 2021;181:1065‐70.
    1. Graber MA, Bailey O. The wizard behind the curtain: programmers as providers. Philos. Ethics Humanit. Med. 2016; 11: 4.

Source: PubMed

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