Machine learning in diagnostic support in medical emergency departments.

Autor: Brasen CL; Department of Biochemistry and Immunology, Lillebaelt Hospital, University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark. claus.lohman.brasen@rsyd.dk.; Faculty of Health Sciences, Department of Regional Health Research, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark. claus.lohman.brasen@rsyd.dk., Andersen ES; Department of Biochemistry and Immunology, Lillebaelt Hospital, University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark.; Faculty of Health Sciences, Department of Regional Health Research, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark., Madsen JB; Department of Biochemistry and Immunology, Lillebaelt Hospital, University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark., Hastrup J; Department of Biochemistry and Immunology, Lillebaelt Hospital, University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark., Christensen H; Department of Biochemistry and Immunology, Lillebaelt Hospital, University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark., Andersen DP; Department of Emergency, Kolding Hospital, Lillebaelt Hospital, University Hospital of Southern Denmark, Sygehusvej 24, 6000, Kolding, Denmark., Lind PM; Department of Biochemistry and Immunology, Lillebaelt Hospital, University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark., Mogensen N; Department of Biochemistry and Immunology, Lillebaelt Hospital, University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark., Madsen PH; Department of Medicine, Kolding Hospital, Lillebaelt Hospital, University Hospital of Southern Denmark, Sygehusvej 24, 6000, Kolding, Denmark.; Emergency, Acute Care and Trauma Centre, Odense University Hospital, J. B. Winsløws Vej 4, 5000, Odense, Denmark., Christensen AF; Department of Medicine, Kolding Hospital, Lillebaelt Hospital, University Hospital of Southern Denmark, Sygehusvej 24, 6000, Kolding, Denmark., Madsen JS; Department of Biochemistry and Immunology, Lillebaelt Hospital, University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark.; Faculty of Health Sciences, Department of Regional Health Research, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark., Ejlersen E; Department of Medicine, Vejle Hospital, Lillebaelt Hospital, University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark., Brandslund I; Department of Biochemistry and Immunology, Lillebaelt Hospital, University Hospital of Southern Denmark, Beriderbakken 4, 7100, Vejle, Denmark.; Faculty of Health Sciences, Department of Regional Health Research, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Aug 02; Vol. 14 (1), pp. 17889. Date of Electronic Publication: 2024 Aug 02.
DOI: 10.1038/s41598-024-66837-w
Abstrakt: Diagnosing 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 including the 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 -curves in 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 machine-learning 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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