Artificial intelligence(AI),a branch of machine learning(ML)has been increasingly employed in the research of trauma in various aspects.Hemorrhage is the most common cause of trauma-related death.To better elucidate t...Artificial intelligence(AI),a branch of machine learning(ML)has been increasingly employed in the research of trauma in various aspects.Hemorrhage is the most common cause of trauma-related death.To better elucidate the current role of AI and contribute to future development of ML in trauma care,we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumatic hemorrhage.A literature search was carried out on PubMed and Google scholar.Titles and abstracts were screened and,if deemed appropriate,the full articles were reviewed.We included 89 studies in the review.These studies could be grouped into five areas:(1)prediction of outcomes;(2)risk assessment and injury severity for triage;(3)prediction of transfusions;(4)detection of hemorrhage;and(5)prediction of coagulopathy.Performance analysis of ML in comparison with current standards for trauma care showed that most studies demonstrated the benefits of ML models.However,most studies were retrospective,focused on prediction of mortality,and development of patient outcome scoring systems.Few studies performed model assessment via test datasets obtained from different sources.Prediction models for transfusions and coagulopathy have been developed,but none is in widespread use.AI-enabled ML-driven technology is becoming integral part of the whole course of trauma care.Comparison and application of ML algorithms using different datasets from initial training,testing and validation in prospective and randomized controlled trials are warranted for provision of decision support for individualized patient care as far forward as possible.展开更多
Hemorrhage is the leading cause of preventable death in combat trauma and the secondary cause of death in civilian trauma.A significant number of deaths due to hemorrhage occur before and in the first hour after hospi...Hemorrhage is the leading cause of preventable death in combat trauma and the secondary cause of death in civilian trauma.A significant number of deaths due to hemorrhage occur before and in the first hour after hospital arrival.A literature search was performed through PubMed,Scopus,and Institute of Scientific Information databases for English language articles using terms relating to hemostatic agents,prehospital,battlefield or combat dressings,and prehospital hemostatic resuscitation,followed by cross-reference searching.Abstracts were screened to determine relevance and whether appropriate further review of the original articles was warranted.Based on these findings,this paper provides a review of a variety of hemostatic agents ranging from clinically approved products for human use to newly developed concepts with great potential for use in prehospital settings.These hemostatic agents can be administered either systemically or locally to stop bleeding through different mechanisms of action.Comparisons of current hemostatic products and further directions for prehospital hemorrhage control are also discussed.展开更多
基金Defence Research and Development Canada,Program Activity PEOPLE_014.
文摘Artificial intelligence(AI),a branch of machine learning(ML)has been increasingly employed in the research of trauma in various aspects.Hemorrhage is the most common cause of trauma-related death.To better elucidate the current role of AI and contribute to future development of ML in trauma care,we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumatic hemorrhage.A literature search was carried out on PubMed and Google scholar.Titles and abstracts were screened and,if deemed appropriate,the full articles were reviewed.We included 89 studies in the review.These studies could be grouped into five areas:(1)prediction of outcomes;(2)risk assessment and injury severity for triage;(3)prediction of transfusions;(4)detection of hemorrhage;and(5)prediction of coagulopathy.Performance analysis of ML in comparison with current standards for trauma care showed that most studies demonstrated the benefits of ML models.However,most studies were retrospective,focused on prediction of mortality,and development of patient outcome scoring systems.Few studies performed model assessment via test datasets obtained from different sources.Prediction models for transfusions and coagulopathy have been developed,but none is in widespread use.AI-enabled ML-driven technology is becoming integral part of the whole course of trauma care.Comparison and application of ML algorithms using different datasets from initial training,testing and validation in prospective and randomized controlled trials are warranted for provision of decision support for individualized patient care as far forward as possible.
基金Canadian Forces Health Services and Defence Research and Development Canada for their support
文摘Hemorrhage is the leading cause of preventable death in combat trauma and the secondary cause of death in civilian trauma.A significant number of deaths due to hemorrhage occur before and in the first hour after hospital arrival.A literature search was performed through PubMed,Scopus,and Institute of Scientific Information databases for English language articles using terms relating to hemostatic agents,prehospital,battlefield or combat dressings,and prehospital hemostatic resuscitation,followed by cross-reference searching.Abstracts were screened to determine relevance and whether appropriate further review of the original articles was warranted.Based on these findings,this paper provides a review of a variety of hemostatic agents ranging from clinically approved products for human use to newly developed concepts with great potential for use in prehospital settings.These hemostatic agents can be administered either systemically or locally to stop bleeding through different mechanisms of action.Comparisons of current hemostatic products and further directions for prehospital hemorrhage control are also discussed.
