Identifying the need for infection-related consultations in intensive care patients using machine learning models

Leslie R. Zwerwer*, Christian F. Luz, Dimitrios Soudis, Nicoletta Giudice, Maarten W.N. Nijsten, Corinna Glasner, Maurits H. Renes, Bhanu Sinha

*Corresponding author voor dit werk

OnderzoeksoutputAcademicpeer review

1 Citaat (Scopus)
23 Downloads (Pure)


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.

Originele taal-2English
Aantal pagina's13
TijdschriftScientific Reports
Nummer van het tijdschrift1
StatusE-pub ahead of print - 28-jan.-2024


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