TY - JOUR
T1 - Graph convolutional network-based fusion model to predict risk of hospital acquired infections
AU - Tariq, Amara
AU - Lancaster, Lin
AU - Elugunti, Praneetha
AU - Siebeneck, Eric
AU - Noe, Katherine
AU - Borah, Bijan
AU - Moriarty, James
AU - Banerjee, Imon
AU - Patel, Bhavik N.
N1 - Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - Objective: Hospital acquired infections (HAIs) are one of the top 10 leading causes of death within the United States. While current standard of HAI risk prediction utilizes only a narrow set of predefined clinical variables, we propose a graph convolutional neural network (GNN)-based model which incorporates a wide variety of clinical features. Materials and Methods: Our GNN-based model defines patients' similarity based on comprehensive clinical history and demographics and predicts all types of HAI rather than focusing on a single subtype. An HAI model was trained on 38 327 unique hospitalizations while a distinct model for surgical site infection (SSI) prediction was trained on 18 609 hospitalization. Both models were tested internally and externally on a geographically disparate site with varying infection rates. Results: The proposed approach outperformed all baselines (single-modality models and length-of-stay [LoS]) with achieved area under the receiver operating characteristics of 0.86 [0.84-0.88] and 0.79 [0.75-0.83] (HAI), and 0.79 [0.75-0.83] and 0.76 [0.71-0.76] (SSI) for internal and external testing. Cost-effective analysis shows that the GNN modeling dominated the standard LoS model strategy on the basis of lower mean costs ($1651 vs $1915). Discussion: The proposed HAI risk prediction model can estimate individualized risk of infection for patient by taking into account not only the patient's clinical features, but also clinical features of similar patients as indicated by edges of the patients' graph. Conclusions: The proposed model could allow prevention or earlier detection of HAI, which in turn could decrease hospital LoS and associated mortality, and ultimately reduce the healthcare cost.
AB - Objective: Hospital acquired infections (HAIs) are one of the top 10 leading causes of death within the United States. While current standard of HAI risk prediction utilizes only a narrow set of predefined clinical variables, we propose a graph convolutional neural network (GNN)-based model which incorporates a wide variety of clinical features. Materials and Methods: Our GNN-based model defines patients' similarity based on comprehensive clinical history and demographics and predicts all types of HAI rather than focusing on a single subtype. An HAI model was trained on 38 327 unique hospitalizations while a distinct model for surgical site infection (SSI) prediction was trained on 18 609 hospitalization. Both models were tested internally and externally on a geographically disparate site with varying infection rates. Results: The proposed approach outperformed all baselines (single-modality models and length-of-stay [LoS]) with achieved area under the receiver operating characteristics of 0.86 [0.84-0.88] and 0.79 [0.75-0.83] (HAI), and 0.79 [0.75-0.83] and 0.76 [0.71-0.76] (SSI) for internal and external testing. Cost-effective analysis shows that the GNN modeling dominated the standard LoS model strategy on the basis of lower mean costs ($1651 vs $1915). Discussion: The proposed HAI risk prediction model can estimate individualized risk of infection for patient by taking into account not only the patient's clinical features, but also clinical features of similar patients as indicated by edges of the patients' graph. Conclusions: The proposed model could allow prevention or earlier detection of HAI, which in turn could decrease hospital LoS and associated mortality, and ultimately reduce the healthcare cost.
KW - Clostridioides difficile
KW - central line-associated bloodstream infection
KW - cost-effectiveness
KW - graph neural network
KW - hospital acquired infection
KW - methicillin-resistant Staphylococcus aureus
KW - surgical site infection
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U2 - 10.1093/jamia/ocad045
DO - 10.1093/jamia/ocad045
M3 - Article
C2 - 37027831
AN - SCOPUS:85159760293
SN - 1067-5027
VL - 30
SP - 1056
EP - 1067
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 6
ER -