Enhancing the clinical relevance of haemorrhage prediction models in trauma

Sankalp Tandle; Jared M. Wohlgemut; Max E. R. Marsden; Erhan Pisirir; Evangelia Kyrimi; Rebecca S. Stoner; William Marsh; Zane B. Perkins; Nigel R. M. Tai

doi:10.1186/s40779-023-00476-6

Your Location：

Home >

Browse articles >

Enhancing the clinical relevance of haemorrhage prediction models in trauma

COMMENTARY | Updated：2025-12-13

- Enhancing the clinical relevance of haemorrhage prediction models in trauma
- Military Medical Research Vol. 11, Issue 3, Pages: 467-468(2024)
- Affiliations：
  
  1.Centre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London E1 2AT, UK
  2.The Royal London Hospital, Barts Health NHS Trust, London E1 1FR, UK
  3.Academic Department of Military Surgery and Trauma, Research and Clinical Innovation, The Royal Centre for Defence Medicine, Birmingham B15 2WB, UK
  4.Department of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
- Author bio：
  
  *sankalp.tandle1@nhs.net
- Funds：
- DOI：10.1186/s40779-023-00476-6
  CLC：
- Published：2024-06
- Accepted：
Scan QR Code
Cite this article as: Tandle S, Wohlgemut JM, Marsden MER, Pisirir E, Kyrimi E, Stoner RS, et al. Enhancing the clinical relevance of haemorrhage prediction models in trauma. Mil Med Res. 2023;10(1):43.
DOI：

Cite this article as: Tandle S, Wohlgemut JM, Marsden MER, Pisirir E, Kyrimi E, Stoner RS, et al. Enhancing the clinical relevance of haemorrhage prediction models in trauma. Mil Med Res. 2023;10(1):43. DOI： 10.1186/s40779-023-00476-6.

摘要

Abstract

关键词

Keywords

references

Peng HT , Siddiqui MM , Rhind SG , Zhang J , Teodoro da Luz L , Beckett A . Artificial intelligence and machine learning for hemorrhagic trauma care . Mil Med Res . 2023 ; 10 ( 1 ): 6 .

El-Menyar A , Mekkodathil A , Abdelrahman H , Latifi R , Galwankar S , Al-Thani H , et al . Review of existing scoring systems for massive blood transfusion in trauma patients: where do we stand? Shock . 2019 ; 52 ( 3 ): 288 – 99 .

Ho AM , Zamora JE , Holcomb JB , Ng CS , Karmakar MK , Dion PW . The many faces of survivor bias in observational studies on trauma resuscitation requiring massive transfusion . Ann Emerg Med . 2015 ; 66 ( 1 ): 45 – 8 .

Holcomb JB , Moore EE , Sperry JL , Jansen JO , Schreiber MA , Del Junco DJ , et al . Evidence-based and clinically relevant outcomes for hemorrhage control trauma trials . Ann Surg . 2021 ; 273 ( 3 ): 395 – 401 .

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Artificial intelligence and machine learning for hemorrhagic trauma care

PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer

Hemopneumothorax detection through the process of artificial evolution - a feasibility study

Research on Wheel/Rail Action of Heavy Haul Train

Research on Driving Equipment Suspension Parameters and the Structures of Locomotives with Motor Elastically Mounted on the Frame

Related Author

Henry T. Peng

M. Musaab Siddiqui

Shawn G. Rhind

Jing Zhang

Luis Teodoro da Luz

Andrew Beckett

Yu Wang

Yong-Bo Xiang

Related Institution

Defence Research and Development Canada, Toronto Research Centre

Sunnybrook Health Sciences Centre

. Michael’s Hospital

Royal Canadian Medical Services

Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

⁰