Version 1
: Received: 10 June 2022 / Approved: 10 June 2022 / Online: 10 June 2022 (04:43:07 CEST)
How to cite:
Faucher, M.; Casie Chetty, S.; Shokouhi, S.; Barnes, G.; Rahmani, K.; Calvert, J.; Mao, Q. Early Prediction of Ventilator-Associated Pneumonia in ICU Patients Using An Interpretable Machine Learning Algorithm. Preprints2022, 2022060149. https://doi.org/10.20944/preprints202206.0149.v1
Faucher, M.; Casie Chetty, S.; Shokouhi, S.; Barnes, G.; Rahmani, K.; Calvert, J.; Mao, Q. Early Prediction of Ventilator-Associated Pneumonia in ICU Patients Using An Interpretable Machine Learning Algorithm. Preprints 2022, 2022060149. https://doi.org/10.20944/preprints202206.0149.v1
Faucher, M.; Casie Chetty, S.; Shokouhi, S.; Barnes, G.; Rahmani, K.; Calvert, J.; Mao, Q. Early Prediction of Ventilator-Associated Pneumonia in ICU Patients Using An Interpretable Machine Learning Algorithm. Preprints2022, 2022060149. https://doi.org/10.20944/preprints202206.0149.v1
APA Style
Faucher, M., Casie Chetty, S., Shokouhi, S., Barnes, G., Rahmani, K., Calvert, J., & Mao, Q. (2022). Early Prediction of Ventilator-Associated Pneumonia in ICU Patients Using An Interpretable Machine Learning Algorithm. Preprints. https://doi.org/10.20944/preprints202206.0149.v1
Chicago/Turabian Style
Faucher, M., Jacob Calvert and Qingqing Mao. 2022 "Early Prediction of Ventilator-Associated Pneumonia in ICU Patients Using An Interpretable Machine Learning Algorithm" Preprints. https://doi.org/10.20944/preprints202206.0149.v1
Abstract
(1) Background: Ventilator-associated pneumonia (VAP) causes high mortality among patients with respiratory disease and imposes major burdens on healthcare infrastructure. Models that use electronic health record data to predict the onset of VAP may spur earlier treatment and improve patient outcomes. We developed and studied the performance of interpretable machine learning (ML) models that predict the onset of VAP from electronic health records (EHRs); (2) Methods: We trained Logistic Regression (LR), full feature Explainable Boosting Machine (fEBM), and eXtreme Gradient Boosting (XGBoost) ML models on data from the MIMIC- III (v1.3) database. Model performance was measured by area under the receiver operating characteristic curves (AUCs). We trained a minimal-feature EBM model (mEBM) with features derived from white blood cell (WBC) counts, duration of ventilation, and Glasgow Coma Scale (GCS). Finally, model robustness was evaluated on randomly sparsified EHR datasets; (3) Results: The fEBM model outperformed the XGBoost and LR models at 24 hours post-intubation. The mEBM model maintained an AUC of 0.893. The fEBM model performance remained robust on sparsified datasets; (4) Conclusions: Our novel interpretable ML algorithm reliably predicts the onset of VAP in intubated patients. Integration of this EBM-based model into clinical practice may enable clinicians to better anticipate and prevent VAP.
Medicine and Pharmacology, Pulmonary and Respiratory Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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