How machine learning facilitates decision making in emergency departments: Modelling diagnostic test orders

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dc.authoridAtaman, Mustafa Gokalp / 0000-0003-4468-0020
dc.authoridsariyer, gorkem / 0000-0002-8290-2248
dc.authorscopusid57189867008
dc.authorscopusid57192943136
dc.authorwosidAtaman, Mustafa Gokalp/O-4644-2017
dc.authorwosidsariyer, gorkem/AAA-1524-2019
dc.contributor.authorSarıyer, Görkem
dc.contributor.authorAtaman, Mustafa Gokalp
dc.date.accessioned2022-02-15T16:58:27Z
dc.date.available2022-02-15T16:58:27Z
dc.date.issued2021
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractObjectives Since emergency departments (EDs) are responsible for providing initial care for patients who may need urgent medical care, they are highly sensitive to increased patient delays. A key factor that increases patient delays is ordering diagnostic tests. Therefore, understanding the factors increasing diagnostic test orders and proposing efficient models may facilitate decision making in EDs. Methods Month and week of the year, day of the week, and daily numbers of patients encoded based on 21 different ICD-10 codes were used as input variables. Daily test frequencies of patients requiring tests from laboratory and imaging services were modelled separately by linear regression models. Although significance of the input variables was identified based on these models, obtained forecasts and residuals were further processed by machine learning techniques to obtain hybrid models. Results Day of the week, and number of patients with ICD-10 codes of 'A00-B99', 'I00-I99', 'J00-J99', 'M00-M99' and 'R00-R99' were significant in both test types. In addition to these, although daily patient frequencies with 'H60-H95', 'N00-N99' and 'O00-O9A' were significant for laboratory services, 'L00-L99', 'S00-T88' and 'Z00-Z99' were significant for imaging services. Although prediction accuracies of regression models were, respectively, as 93.658% and 95.028% for laboratory and imaging services modelling, they increased to 99.997% and 99.995% with the machine learning-integrated hybrid model. Conclusion The significant factors identified here can predict increases in use of laboratory and imaging services. This could enable these services to be prepared in advance to reduce ED patient delays, thereby reducing ED overcrowding. The proposed model may also be efficiently used for decision making.en_US
dc.identifier.doi10.1111/ijcp.14980
dc.identifier.issn1368-5031
dc.identifier.issn1742-1241
dc.identifier.issue12en_US
dc.identifier.pmid34637191en_US
dc.identifier.scopus2-s2.0-85117460801en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1111/ijcp.14980
dc.identifier.urihttps://hdl.handle.net/20.500.14034/409
dc.identifier.volume75en_US
dc.identifier.wosWOS:000709636600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.journalInternational Journal Of Clinical Practiceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBig Dataen_US
dc.subjectManagementen_US
dc.subjectInformationen_US
dc.subjectDiseasesen_US
dc.subjectTimesen_US
dc.titleHow machine learning facilitates decision making in emergency departments: Modelling diagnostic test ordersen_US
dc.typeArticleen_US

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