Summary
Background
Conventional surgical site infection (SSI) surveillance is labour-intensive. We aimed
to develop machine learning (ML) models for the surveillance of SSIs for colon surgery
and to assess whether the ML could improve surveillance process efficiency.
Methods
This study included cases who underwent colon surgery at a tertiary center between
2013 and 2014. Logistic regression and four ML algorithms including random forest
(RF), gradient boosting (GB), and neural networks (NNs) with or without recursive
feature elimination (RFE) were first trained on the entire cohort, and then re-trained
on cases selected based on a previous rule-based algorithm. We assessed model performance
based on the area under the curve (AUC), sensitivity, and positive predictive value
(PPV). The estimated proportion of reduction in workload for chart review based on
the ML models was evaluated and compared with the conventional method.
Results
At a sensitivity of 95%, the NN with RFE using 29 variables had the best performance
with an AUC of 0.963 and PPV of 21.1%. When combining both the rule-based algorithm
and ML algorithms, the NN with RFE using 19 variables had a higher PPV (28.9%) than
with the ML algorithm alone, which could decrease the number of cases requiring chart
review by 83.9% compared with the conventional method.
Conclusion
We demonstrated that ML can improve the efficiency of SSI surveillance for colon surgery
by decreasing the burden of chart review while providing high sensitivity. In particular,
the hybrid approach of ML with a rule-based algorithm showed the best performance
in terms of PPV.
Keywords
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Article info
Publication history
Published online: April 21, 2023
Accepted:
March 24,
2023
Received in revised form:
March 21,
2023
Received:
November 10,
2022
Publication stage
In Press Journal Pre-ProofIdentification
Copyright
© 2023 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.
