| 1 |
World Health Organization. Global status report on road safety 2018 [R/OL]. Geneva: WHO, 2018 [2024-11-08].
|
| 2 |
James SL, Castle CD, Dingels ZV, et al. Global injury morbidity and mortality from 1990 to 2017: results from the Global Burden of Disease Study 2017 [J]. Inj Prev, 2020, 26(Suppl 1): i96-i114.
|
| 3 |
GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017 [J]. Lancet, 2018, 392(10159): 1736-1788.
|
| 4 |
Alvarez-Nebreda ML, Weaver MJ, Uribe-Leitz T, et al. Epidemiology of pelvic and acetabular fractures in the USA from 2007 to 2014 [J]. Osteoporos Int, 2023, 34(3): 527-537.
|
| 5 |
Ilkhani S, Comrie CE, Pinkes N, et al. Beyond surviving: a scoping review of collaborative care models to inform the future of postdischarge trauma care [J]. J Trauma Acute Care Surg, 2024, 97(4): e41-e52.
|
| 6 |
Abdulai AF, Naghdali H, Tekie Ghirmay E, et al. Trauma-informed care in digital health technologies: protocol for a scoping review [J]. JMIR Research Protocols, 2023, 12: e46842.
|
| 7 |
Cannon JW, Gruen DS, Zamora R, et al. Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies [J]. Commun Med (Lond), 2024, 4(1): 113.
|
| 8 |
Peng HT, Siddiqui MM, Rhind SG, et al. Artificial intelligence and machine learning for hemorrhagic trauma care [J]. Mil Med Res, 2023, 10(1): 6.
|
| 9 |
杨晓光, 王玉妹, 王晓岩, 等. 5G+物联网冬奥会医疗保障指挥调度平台在危重孕产妇抢救中的应用展望 [J/OL]. 中华重症医学电子杂志, 2022, 8(3): 253-256.
|
| 10 |
McGinley A, Pearse RM. A national early warning score for acutely ill patients [J]. BMJ, 2012, 345: e5310.
|
| 11 |
Mun F, Ringenbach K, Baer B, et al. Factors influencing geriatric orthopaedic trauma mortality [J]. Injury, 2022, 53(3): 919-924.
|
| 12 |
Schwartz AM, Staley CA, Wilson JM, et al. High acuity polytrauma centers in orthopaedic trauma: decreasing patient mortality with effective resource utilization [J]. Injury, 2020, 51(10): 2235-2240.
|
| 13 |
Kim HJ, Ko RE, Lim SY, et al. Sepsis alert systems, mortality, and adherence in emergency departments: a systematic review and meta-analysis [J]. JAMA Netw Open, 2024, 7(7): e2422823.
|
| 14 |
Bassin L, Raubenheimer J, Bell D. The implementation of a real time early warning system using machine learning in an Australian hospital to improve patient outcomes [J]. Resuscitation, 2023, 188: 109821.
|
| 15 |
Kollef MH, Chen Y, Heard K, et al. A randomized trial of real-time automated clinical deterioration alerts sent to a rapid response team [J]. J Hosp Med, 2014, 9(7): 424-429.
|
| 16 |
Levin MA, Kia A, Timsina P, et al. Real-time machine learning alerts to prevent escalation of care: a nonrandomized clustered pragmatic clinical trial [J]. Crit Care Med, 2024, 52(7): 1007-1020.
|
| 17 |
Ye C, Wang O, Liu M, et al. A real-time early warning system for monitoring inpatient mortality risk: prospective study using electronic medical record data [J]. J Med Internet Res, 2019, 21(7): e13719.
|
| 18 |
Blythe R, Parsons R, White NM, et al. A scoping review of real-time automated clinical deterioration alerts and evidence of impacts on hospitalised patient outcomes [J]. BMJ Qual Saf, 2022, 31(10): 725-734.
|
| 19 |
Van der Vegt AH, Campbell V, Mitchell I, et al. Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain [J]. J Am Med Inform Assoc, 2024, 31(2): 509-524.
|
| 20 |
Sprogis SK, Currey J, Jones D, et al. Use of the pre-medical emergency team tier of rapid response systems: a scoping review [J]. Intensive Crit Care Nurs, 2021, 65: 103041.
|
| 21 |
Wang Y, Jiang MY, He M, et al. Design and implementation of an inpatient fall risk management information system [J]. JMIR Med Inform, 2024, 12: e46501.
|