[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127103-en":3,"doc-seo-127103-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":11},127103,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning to predict haemorrhage after injury - So many models, so little dynamism","Accurately predicting the need for blood transfusion in bleeding trauma patients remains a critical challenge in emergency care. Machine learning (ML) models can support decision-making, yet a research-to-practice gap persists due to limited treatment of the dynamic character of clinical data. A scoping review examines ML approaches using time-varying inputs to forecast transfusion needs, emphasizing difficulties in obtaining high-quality time-series data from electronic health records (EHRs) and the need for explainable, clinically interpretable AI to improve real-world utility.","Intelligence-Based Medicine 11 (2025) 100241  \nContents lists available at ScienceDirect  \nIntelligence-Based Medicine  \njournal [homepage:](homepage: www.sciencedirect.com/journal/intelligence-based-medicine)[ www.sciencedirect.com/journal/intelligence-based-medicine](homepage: www.sciencedirect.com/journal/intelligence-based-medicine)  \nMachine learning to predict haemorrhage after injury: So many models, so little dynamism  \nA R T I C L E I N F O  \nKeywords:  \nArtificial intelligence Trauma Haemorrhage Machine learning Transfusion  \nA B S T R A C T  \nAccurately predicting the need for blood transfusion in bleeding patients remains a critical challenge in emergency care. Machine learning (ML) models show promise for improving decision support in these scenarios, but a gap remains between research and practical application. Existing models frequently overlook the dynamic nature of clinical data, hindering their ability to provide accurate predictions for blood transfusion needs in emergency settings. We conducted a scoping review to examine ML models that integrate time-varying variables to predict blood transfusion needs in trauma patients. We discuss challenges in data collection, particularly the limitations of electronic health records (EHRs) in capturing high-quality time-series data and emphasise the need for explainable artificial intelligence (AI). We suggest future directions for research that include advancing computational approaches, improving data collection, and enhancing the interpretability of ML models to ensure their clinical relevance and utility.  \nLetter to the editor  \nThe management of bleeding patients in emergency settings is a critical challenge that requires timely and informed decision-making [1]. Identifying high risk patients in need of blood transfusion can be difficult for clinicians and would benefit from decision support [2]. While machine learning models hold promise for predicting bleeding risk [3,4], a significant chasm exists between research and real-world application [5,6]. We argue here that this gap is partly attributable to the failure of these models to incorporate key decision making factors clinicians use to diagnose haemorrhage.  \nThe resuscitation of bleeding patients requires a nuanced approach based on the dynamic assessment of vital signs and point of care blood analysis, which can rapidly evolve during treatment [7]. Trends in these variables provide crucial insights into a patient’s evolving clinical status and guide timely interventions, particularly in cases of active bleeding. A rise in heart rate and lactate levels coupled with progressive hypotension after injury is suggestive of haemorrhage, prompting emergent blood transfusion. Furthermore, the patient’s response to this fluid resuscitation is fundamental to assessing their degree of ongoing haemorrhage. The dynamic interplay of laboratory values, physiological markers, and response to therapy guides clinicians in their decision-making [7].  \nWe conducted a scoping review to identify machine learning (ML) models that incorporate changes in patient variables over time to predict the need for blood transfusion. The Joanna Briggs Institute (JBI) scoping review framework was used to guide this review [8]. Publications were eligible for inclusion if they reported a ML-derived prediction model for blood transfusion that had dynamic inputs. We defined a dynamic input as a variable that included a change over time. Our search was performed in four databases: Medline, Web of Science, Embase, and Cochrane. The search terms used were ((“trauma” or “injury” or“emergency”) and (“artificial intelligence” or “machine learning” or“predictive modelling” or “algorithm”) and (“outcome prediction” or  \n“prognosis” or “predictive analytics”) and (“haemorrhage” or “blood loss” or “transfusion”)).  \nThis strategy yielded seven studies for inclusion (Fig. 1). Screening was conducted independently by two authors (GS and YA) and any conflicti","cbCaicwvAGcVU6yk","https://ap.wps.com/l/cbCaicwvAGcVU6yk","pdf",837774,1,3,"English","en",105,"# Contents\n## Abstract\n## Letter to the editor\n## Scoping review methods\n## Included studies and findings\n## Rationale and future directions","[{\"question\":\"Why is predicting blood transfusion need in emergency trauma care difficult?\",\"answer\":\"Clinicians must make timely decisions under rapidly changing conditions, and many ML models do not adequately reflect the dynamic nature of clinical data in these settings.\"},{\"question\":\"What was the purpose and approach of the scoping review?\",\"answer\":\"The review identified ML-derived prediction models for blood transfusion that used dynamic inputs, guided by the Joanna Briggs Institute (JBI) scoping review framework and searched multiple databases.\"},{\"question\":\"What key problem did the review find in existing haemorrhage prediction models?\",\"answer\":\"None of the included studies—despite describing many approaches—contained models that incorporated dynamic variables as defined by time-varying inputs.\"}]","Machine learning to predict haemorrhage after injury - 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