Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics

被引:3
作者
Garrido, Nicolas J. [1 ,2 ]
Gonzalez-Martinez, Felix [2 ,3 ,4 ]
Losada, Susana [3 ]
Plaza, Adrian [3 ]
del Olmo, Eneida [3 ]
Mateo, Jorge [2 ,4 ]
机构
[1] Virgen Luz Hosp, Internal Med, Cuenca 16002, Spain
[2] Univ Castilla La Mancha, Inst Technol, Expert Med Anal Grp, Cuenca 16071, Spain
[3] Virgen Luz Hosp, Dept Emergency Med, Cuenca 16002, Spain
[4] Inst Invest Sanit Castilla La Mancha IDISCAM, Expert Med Anal Grp, Toledo 45071, Spain
关键词
emergency; predictive value of tests; hospital mortality; machine learning; pandemics; SUPPORT VECTOR MACHINE; COVID-19; CLASSIFICATION; VALIDATION;
D O I
10.3390/biomimetics9070440
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Artificial intelligence (AI) systems are already being used in various healthcare areas. Similarly, they can offer many advantages in hospital emergency services. The objective of this work is to demonstrate that through the novel use of AI, a trained system can be developed to detect patients at potential risk of infection in a new pandemic more quickly than standardized triage systems. This identification would occur in the emergency department, thus allowing for the early implementation of organizational preventive measures to block the chain of transmission. Materials and Methods: In this study, we propose the use of a machine learning system in emergency department triage during pandemics to detect patients at the highest risk of death and infection using the COVID-19 era as an example, where rapid decision making and comprehensive support have becoming increasingly crucial. All patients who consecutively presented to the emergency department were included, and more than 89 variables were automatically analyzed using the extreme gradient boosting (XGB) algorithm. Results: The XGB system demonstrated the highest balanced accuracy at 91.61%. Additionally, it obtained results more quickly than traditional triage systems. The variables that most influenced mortality prediction were procalcitonin level, age, and oxygen saturation, followed by lactate dehydrogenase (LDH) level, C-reactive protein, the presence of interstitial infiltrates on chest X-ray, and D-dimer. Our system also identified the importance of oxygen therapy in these patients. Conclusions: These results highlight that XGB is a useful and novel tool in triage systems for guiding the care pathway in future pandemics, thus following the example set by the well-known COVID-19 pandemic.
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页数:20
相关论文
共 98 条
[1]   COVID-19 image classification using deep learning: Advances, challenges and opportunities [J].
Aggarwal, Priya ;
Mishra, Narendra Kumar ;
Fatimah, Binish ;
Singh, Pushpendra ;
Gupta, Anubha ;
Joshi, Shiv Dutt .
COMPUTERS IN BIOLOGY AND MEDICINE, 2022, 144
[2]   Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model [J].
Alkhammash, Eman H. ;
Assiri, Sara Ahmad ;
Nemenqani, Dalal M. ;
Althaqafi, Raad M. M. ;
Hadjouni, Myriam ;
Saeed, Faisal ;
Elshewey, Ahmed M. .
BIOMIMETICS, 2023, 8 (06)
[3]   Impact of Artificial Intelligence on COVID-19 Pandemic: A Survey of Image Processing, Tracking of Disease, Prediction of Outcomes, and Computational Medicine [J].
Almotairi, Khaled H. ;
Hussein, Ahmad MohdAziz ;
Abualigah, Laith ;
Abujayyab, Sohaib K. M. ;
Mahmoud, Emad Hamdi ;
Ghanem, Bassam Omar ;
Gandomi, Amir H. .
BIG DATA AND COGNITIVE COMPUTING, 2023, 7 (01)
[4]   Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings [J].
Amgad, Mohamed ;
Atteya, LameesA ;
Hussein, Hagar ;
Mohammed, Kareem Hosny ;
Hafiz, Ehab ;
Elsebaie, Maha A. T. ;
Mobadersany, Pooya ;
Manthey, David ;
Gutman, David A. ;
Elfandy, Habiba ;
Cooper, Lee A. D. .
BIOINFORMATICS, 2022, 38 (02) :513-519
[5]   Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study [J].
An, Chansik ;
Lim, Hyunsun ;
Kim, Dong-Wook ;
Chang, Jung Hyun ;
Choi, Yoon Jung ;
Kim, Seong Woo .
SCIENTIFIC REPORTS, 2020, 10 (01)
[6]   Percentile-Based Adaptive Immune Plasma Algorithm and Its Application to Engineering Optimization [J].
Aslan, Selcuk ;
Demirci, Sercan ;
Oktay, Tugrul ;
Yesilbas, Erdal .
BIOMIMETICS, 2023, 8 (06)
[7]   Artificial intelligence and its applications in medicine I: introductory background to AI and robotics [J].
Avila-Tomas, J. F. ;
Mayer-Pujadas, M. A. ;
Quesada-Varela, V. J. .
ATENCION PRIMARIA, 2020, 52 (10) :778-784
[8]   Comparison of COVID-19 and influenza characteristics [J].
Bai, Yu ;
Tao, Xiaonan .
JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B, 2021, 22 (02) :87-98
[9]  
Bajwa Junaid, 2021, Future Healthc J, V8, pe188, DOI 10.7861/fhj.2021-0095
[10]  
Bansal M., 2022, Decis. Analytics J, V3, P100071, DOI DOI 10.1016/J.DAJOUR.2022.100071