[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118228-en":3,"doc-seo-118228-105":30,"detail-sidebar-cat-0-en-105":95},{"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},118228,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Machine learning in diagnostic support in medical emergency departments","Machine learning can help physicians manage the complexity of diagnosis in medical emergency departments, a challenge expected to grow with ageing populations and limited resources. A cohort study trained 19 machine-learning algorithms using 9,190 consecutive patient admissions from two hospitals, based on 260 biochemical analyses supplemented with nurse-registered data. Models were validated on a 20% holdout sample across multiple outcomes, including death and 30-day risk with high AUC performance. Adding admission analyses also reduced subsequent venipunctures within 24 hours by 22%, supporting feasibility and improved patient logistics and outcomes.","University of Southern Denmark  \nMachine learning in diagnostic support in medical emergency departments  \nBrasen, Claus Lohman; Andersen, Eline Sandvig; Madsen, Jeppe Buur; Hastrup, Jens; Christensen, Henry; Andersen, Dorte Patuel; Lind, Pia Margrethe; Mogensen, Nina; Madsen, Poul Henning; Christensen, Anne Friesgaard; Madsen, Jonna Skov; Ejlersen, Ejler; Brandslund, Ivan  \nPublished in: Scientific Reports  \nDOI:  \n10.1038/s41598-024-66837-w  \nPublication date: 2024  \nDocument version:  \nFinal published version  \nDocument license: CC BY  \nCitation for pulished version (APA):  \nBrasen, C. L. , Andersen, E. S. , Madsen, J. B. , Hastrup, J. , Christensen, H. , Andersen, D. P. , Lind, P. M. , Mogensen, N. , Madsen, P. H. , Christensen, A. F. , Madsen, J. S. , Ejlersen, E. , & Brandslund, I. (2024) . Machine learning in diagnostic support in medical emergency departments. Scientific Reports, 14, Article 17889. [https://doi.org/10.1038/s41598-024-66837-w](https://doi.org/10.1038/s41598-024-66837-w)  \nGo to publication entry in University of Southern Denmark's Research Portal  \nTerms of use  \nThis work is brought to you by the University of Southern Denmark.  \nUnless otherwise specified it has been shared according to the terms for self-archiving.  \nIf no other license is stated, these terms apply:  \n• You may download this work for personal use only.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying this open access version  \nIf you believe that this document breaches copyright please contact us providing details and we will investigate your claim. Please direct all enquiries to [puresupport@bib.sdu.dk](puresupport@bib.sdu.dk)  \nDownload date: 01. Aug. 2026  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning in diagnostic support in medical emergency departments  \nClaus Lohman Brasen1,2*, Eline Sandvig Andersen1,2, Jeppe Buur Madsen1, Jens Hastrup1, Henry Christensen1, Dorte Patuel Andersen3, Pia Margrethe Lind1, Nina Mogensen1, Poul Henning Madsen4,5, Anne Friesgaard Christensen4, Jonna Skov Madsen1,2,  \nEjler Ejlersen6 & Ivan Brandslund1,2  \nDiagnosing patients in the medical emergency department is complex and this is expected to increase in many countries due to an ageing population. In this study we investigate the feasibility of training machine learning algorithms to assist physicians handling the complex situation in the medical emergency departments. This is expected to reduce diagnostic errors and improve patient logistics and outcome. We included a total of 9,190 consecutive patient admissions diagnosed and treated in two hospitals in this cohort study. Patients had a biochemical workup including blood and urine analyses on clinical decision totaling 260 analyses. After adding nurse-registered data we trained 19 machine learning algorithms on a random 80% sample of the patients and validated the results on the remaining 20%. We trained algorithms for 19 different patient outcomes includingthe main outcomes death in 7 (Area under the Curve (AUC) 91.4%) and 30 days (AUC 91.3%) and safe-discharge(AUC 87.3%). The various algorithms obtained areas under the Receiver Operating Characteristics-curvesin the range of 71.8–96.3% in the holdout cohort (68.3–98.2% in the training cohort) . Performing this list of biochemical analyses at admission also reduced the number of subsequent venipunctures within 24 h from patient admittance by 22%. We have shown that it is possible to develop a list of machinelearning algorithms with high AUC for use in medical emergency departments. Moreover, the study showed that it is possible to reduce the number of venipunctures in this cohort.  \nAbbreviations  \nROC Receiver operating charateristics  \nUTI Urinary tract infection ECG Electrocardiogram ABL Acid base laboratory  \nDPIA Data protection impact assessment AUC Area under the curve  \nED Emerg","cbCailYgDUcEaO7U","https://ap.wps.com/l/cbCailYgDUcEaO7U","pdf",1358652,1,11,"English","en",105,"# Background\n## Diagnostic challenges in emergency departments\n## Motivation for machine learning support\n# Methods\n## Cohort design and patient admissions\n## Data sources and feature sets\n## Model training and validation\n# Results\n## Outcome prediction performance\n## Impact on venipunctures after admission\n# Conclusion","[{\"question\":\"What problem does the study target in medical emergency departments?\",\"answer\":\"It addresses the diagnostic complexity in emergency settings caused by fast-paced work, limited staffing, and many diverse presentations, which increases diagnostic error risk.\"},{\"question\":\"How was the machine learning model training performed in the study?\",\"answer\":\"The study included 9,190 consecutive admissions, used biochemical analyses and nurse-registered data, trained 19 algorithms on a random 80% sample, and validated them on the remaining 20%.\"},{\"question\":\"What outcomes and performance metrics were reported?\",\"answer\":\"The algorithms predicted 19 different outcomes, reporting high AUC values for key endpoints such as death at 7 days and 30 days, and safe discharge.\"},{\"question\":\"What operational effect did the admission biochemical analyses have?\",\"answer\":\"Performing the biochemical panel at admission reduced the number of subsequent venipunctures within 24 hours by 22% in the cohort.\"}]","Machine learning in diagnostic support in medical emergency departments | 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problem does the study target in medical emergency departments?","Question",{"text":75,"@type":76},"It addresses the diagnostic complexity in emergency settings caused by fast-paced work, limited staffing, and many diverse presentations, which increases diagnostic error risk.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine learning model training performed in the study?",{"text":80,"@type":76},"The study included 9,190 consecutive admissions, used biochemical analyses and nurse-registered data, trained 19 algorithms on a random 80% sample, and validated them on the remaining 20%.",{"name":82,"@type":73,"acceptedAnswer":83},"What outcomes and performance metrics were reported?",{"text":84,"@type":76},"The algorithms predicted 19 different outcomes, reporting high AUC values for key endpoints such as death at 7 days and 30 days, and safe discharge.",{"name":86,"@type":73,"acceptedAnswer":87},"What operational effect did the admission biochemical analyses 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