[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124139-en":3,"doc-seo-124139-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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":29},124139,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations - Research summary","Police forensic investigations require probabilistic, objective tools to assess whether an impact or assault scenario resulted in traumatic brain injury (TBI). A mechanics-informed machine learning framework is proposed that couples biomechanical simulations of injurious head impacts with machine learning trained on police reports from the Thames Valley Police and the National Crime Agency’s National Injury Database. Biomechanical outputs such as strain and stress distributions are interpreted alongside assault-specific metadata to predict injury outcomes, using two layers (a neural network processing finite-element quantities and an XGBoost model using processed report features).","communications engineering Article  \n[https://doi.org/10.1038/s44172-025-00352-2](https://doi.org/10.1038/s44172-025-00352-2)  \nA mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations  \n Check for updates  \nThe work leverages a range of  \n\n| Yuyang Wei 1, Jeremy Oldroyd2, Phoebe Haste3, Jayaratnam Jayamohan4, Michael Jones5,\u003Cbr>Nicholas Casey6, Jose-Maria Peña7, Sonya Baylis6, Stan Gilmour 2,8  & Antoine Jérusalem 1  |  |\n| --- | --- |\n| Police forensic investigations are not immune to our society’s ubiquitous search for better predictive ability. In the particular and very topical case of Traumatic Brain Injury (TBI), police forensic investigations aim at evaluating whether a given impact or assault scenario led to the clinically observed TBI. This question is traditionally answered by means of forensic biomechanics and neurosurgical expertise which cannot provide a fully objective probabilistic measure. To this end, we propose here a numerical framework-based solution coupling biomechanical simulations of a variety of injurious impacts to machine learning training of police reports provided by the UK’s Thames Valley Police and the National Crime Agency’s National Injury Database. In this approach, the biomechanical predictions of mechanical metrics such as strain and stress distributions are interpreted by the machine learning model by additionally considering assault specific metadata to predict brain injury outcomes. The framework, only taking as input information typically available in police reports, reaches prediction accuracies exceeding 94% for skull fracture, 79% for loss of consciousness and intracranial haemorrhage, and is able to identify the best predictive features for each targeted injury. Overall, the proposed framework offers new avenues for the prediction, directly from police reports, of any TBI related symptom as required by forensic law enforcement investigations. |  |\n| Traumatic Brain Injury(TBI)is a pressing public health concern with major social, economic, and medical implications1–3. The incidence of TBI continues to rise, affecting millions of individuals worldwide and resulting insubstantial mortality and long-term morbidity4–7. In particular, mild TBI is underreported, challenging to diagnose and linked to long-term neurodegenerative processes8–12. As a result, there is an urgent need for accurate assessment tools to predict TBI risk13–16. In the particular context of law enforcement forensic investigations, this challenge is further complicated by its judicial implications. Traditionally, this additional dimension is tackled by the involvement of forensic and clinical experts, asked to evaluate whether an injurious scenario may or may not have caused a TBI, a task that is, by definition, not only dependent on the personal assessment of the expert but also on thedifficult, ifnot impossible, quantitative evaluation ofsaid TBI | in probabilistic terms. The development of a reliable and validated simulation environment that can predict the risk of TBI in various assault scenarios is thus of crucial relevance for improving forensic investigations, supporting law enforcement agencies and enhancing public safety17–21.\u003Cbr>Recent studies have approached this challenge by coupling finite element (FE) models to machine learning. Anderson et al.22 combined FE modelling with network analysis to predict concussion outcomes, highlighting the value of merging biomechanical models with advanced computational methods. Similarly, Cai et al.23 used deep learning models on brain strain data to classify concussions, showing how machine learning can enhance FE models for predicting TBI severity. Here, we propose a different approach making use of a two-layered machine learning framework to process FE simulation outputs. |\n\n1Department of Engineering Science, University of Oxford, Oxford, UK. 2Thames Valley Police, Oxford, UK. 3The Podium Institute for Spo","cbCaiq016mrm9KbL","https://ap.wps.com/l/cbCaiq016mrm9KbL","pdf",1941372,1,12,"English","en",105,"# Background and motivation\n## Limitations of expert-only probabilistic assessment\n## Need for accurate simulation and assessment tools\n# Related work\n## Coupling finite element models with machine learning\n# Proposed mechanics-informed framework\n## Two-layer machine learning structure\n## Data sources and report metadata\n# Model calibration, validation, and outcomes\n## Targeted injury prediction performance","[{\"question\":\"Why is TBI prediction challenging in police and forensic investigations?\",\"answer\":\"Traditional answers rely on forensic biomechanics and clinical expertise that do not provide a fully objective probabilistic measure, and expert judgment alone is not a reliable quantitative solution for causality in probabilistic terms.\"},{\"question\":\"How does the proposed framework combine mechanics and machine learning?\",\"answer\":\"It couples biomechanical simulations of injurious head impacts with machine learning trained on police reports. The model translates mechanical metrics like strain and stress into injury-relevant predictions while incorporating assault-specific metadata.\"},{\"question\":\"What are the two machine learning layers in the framework?\",\"answer\":\"The first layer is a multilayer perceptron trained on finite element simulation outputs to predict mechanical quantities. The second layer uses XGBoost trained on manually postprocessed police reports, taking both report metadata and outputs from the first layer.\"}]","A mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations - Research summary | PDF",1785820663,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-mechanics-informed-machine-learning-framework-for-traumatic-brain-injury-prediction-in-police-and-forensic-investigations-research-summary","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-mechanics-informed-machine-learning-framework-for-traumatic-brain-injury-prediction-in-police-and-forensic-investigations-research-summary/124139/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is TBI prediction challenging in police and forensic investigations?","Question",{"text":75,"@type":76},"Traditional answers rely on forensic biomechanics and clinical expertise that do not provide a fully objective probabilistic measure, and expert judgment alone is not a reliable quantitative solution for causality in probabilistic terms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed framework combine mechanics and machine learning?",{"text":80,"@type":76},"It couples biomechanical simulations of injurious head impacts with machine learning trained on police reports. The model translates mechanical metrics like strain and stress into injury-relevant predictions while incorporating assault-specific metadata.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the two machine learning layers in the framework?",{"text":84,"@type":76},"The first layer is a multilayer perceptron trained on finite element simulation outputs to predict mechanical quantities. The second layer uses XGBoost trained on manually postprocessed police reports, taking both report metadata and outputs from the first layer.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]