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Chee ML, Chee ML, Huang H, Mazzochi K, Taylor K, Wang H, Feng M, Ho AFW, Siddiqui FJ, Ong MEH, Liu N. Artificial intelligence and machine learning in prehospital emergency care: A scoping review. iScience 2023; 26:107407. [PMID: 37609632 PMCID: PMC10440716 DOI: 10.1016/j.isci.2023.107407] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023] Open
Abstract
Our scoping review provides a comprehensive analysis of the landscape of artificial intelligence (AI) applications in prehospital emergency care (PEC). It contributes to the field by highlighting the most studied AI applications and identifying the most common methodological approaches across 106 included studies. The findings indicate a promising future for AI in PEC, with many unique use cases, such as prognostication, demand prediction, resource optimization, and the Internet of Things continuous monitoring systems. Comparisons with other approaches showed AI outperforming clinicians and non-AI algorithms in most cases. However, most studies were internally validated and retrospective, highlighting the need for rigorous prospective validation of AI applications before implementation in clinical settings. We identified knowledge and methodological gaps using an evidence map, offering a roadmap for future investigators. We also discussed the significance of explainable AI for establishing trust in AI systems among clinicians and facilitating real-world validation of AI models.
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Affiliation(s)
- Marcel Lucas Chee
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Mark Leonard Chee
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Haotian Huang
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Katelyn Mazzochi
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Kieran Taylor
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Han Wang
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Andrew Fu Wah Ho
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Fahad Javaid Siddiqui
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Marcus Eng Hock Ong
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
| | - Nan Liu
- Pre-Hospital and Emergency Research Centre, Duke-NUS Medical School, Singapore, Singapore
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
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Gianquintieri L, Brovelli MA, Pagliosa A, Dassi G, Brambilla PM, Bonora R, Sechi GM, Caiani EG. Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9012. [PMID: 35897382 PMCID: PMC9330211 DOI: 10.3390/ijerph19159012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/13/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022]
Abstract
The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.
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Affiliation(s)
- Lorenzo Gianquintieri
- Electronics, Information and Biomedical Engineering Department, Politecnico di Milano, 20133 Milan, Italy;
| | - Maria Antonia Brovelli
- Civil and Environmental Engineering Department, Politecnico di Milano, 20133 Milan, Italy;
- Istituto per il Rilevamento Elettromagnetico dell’Ambiente, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy
| | - Andrea Pagliosa
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Gabriele Dassi
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Piero Maria Brambilla
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Rodolfo Bonora
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Giuseppe Maria Sechi
- Azienda Regionale Emergenza Urgenza (AREU), 20124 Milan, Italy; (A.P.); (G.D.); (P.M.B.); (R.B.); (G.M.S.)
| | - Enrico Gianluca Caiani
- Electronics, Information and Biomedical Engineering Department, Politecnico di Milano, 20133 Milan, Italy;
- Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy
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