How machine learning facilitates decision making in emergency departments: Modelling diagnostic test orders
| Autor: | Mustafa Gökalp Ataman, Görkem Sarıyer |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Diagnostic Tests
Routine Names of the days of the week business.industry Test types Decision Making Diagnostic test Regression analysis General Medicine Overcrowding Machine learning computer.software_genre Medical care Test (assessment) Machine Learning Linear regression Humans Medicine Artificial intelligence Emergency Service Hospital business computer Forecasting |
| Zdroj: | International Journal of Clinical Practice. 75 |
| ISSN: | 1742-1241 1368-5031 |
| Popis: | OBJECTIVES 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. |
| Databáze: | OpenAIRE |
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