Academic Journal
Identifying the need for infection-related consultations in intensive care patients using machine learning models
| العنوان: | Identifying the need for infection-related consultations in intensive care patients using machine learning models |
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| المؤلفون: | Zwerwer, Leslie R., Luz, Christian F., Soudis, Dimitrios, Giudice, Nicoletta, Nijsten, Maarten W.N., Glasner, Corinna, Renes, Maurits H., Sinha, Bhanu |
| المصدر: | Zwerwer , L R , Luz , C F , Soudis , D , Giudice , N , Nijsten , M W N , Glasner , C , Renes , M H & Sinha , B 2024 , ' Identifying the need for infection-related consultations in intensive care patients using machine learning models ' , Scientific Reports , vol. 14 , no. 1 , 2317 . https://doi.org/10.1038/s41598-024-52741-w |
| سنة النشر: | 2024 |
| المجموعة: | University of Groningen research database |
| مصطلحات موضوعية: | Humans, Critical Care, Intensive Care Units, Hospitalization, Referral and Consultation, Machine Learning |
| الوصف: | Infection-related consultations on intensive care units (ICU) have a positive impact on quality of care and clinical outcome. However, timing of these consultations is essential and to date they are typically event-triggered and reactive. Here, we investigate a proactive approach to identify patients in need for infection-related consultations by machine learning models using routine electronic health records. Data was retrieved from a mixed ICU at a large academic tertiary care hospital including 9684 admissions. Infection-related consultations were predicted using logistic regression, random forest, gradient boosting machines, and long short-term memory neural networks (LSTM). Overall, 7.8% of admitted patients received an infection-related consultation. Time-sensitive modelling approaches performed better than static approaches. Using LSTM resulted in the prediction of infection-related consultations in the next clinical shift (up to eight hours in advance) with an area under the receiver operating curve (AUROC) of 0.921 and an area under the precision recall curve (AUPRC) of 0.541. The successful prediction of infection-related consultations for ICU patients was done without the use of classical triggers, such as (interim) microbiology reports. Predicting this key event can potentially streamline ICU and consultant workflows and improve care as well as outcome for critically ill patients with (suspected) infections. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| DOI: | 10.1038/s41598-024-52741-w |
| الاتاحة: | https://hdl.handle.net/11370/2b81092f-d6e5-4707-a6cb-5d93782797f2 https://research.rug.nl/en/publications/2b81092f-d6e5-4707-a6cb-5d93782797f2 https://doi.org/10.1038/s41598-024-52741-w https://pure.rug.nl/ws/files/902736193/s41598-024-52741-w.pdf http://www.scopus.com/inward/record.url?scp=85183436189&partnerID=8YFLogxK |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.F72F5C40 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1038/s41598-024-52741-w |
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