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Supervised learning for infection risk inference using pathology data
Bernard Hernandez1, Pau Herrero2, Timothy Miles Rawson3
1Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, B422 Bessemer Building, South Kensington Campus, London, SW7 2AZ, UK. b.hernandez-perez@imperial.ac.uk.
Background:
Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been proven to enhance quality of care by promoting change in prescription practices through antimicrobial selection advice. However, bypassing an initial assessment to determine the existence of an underlying disease that justifies the need of antimicrobial therapy might lead to indiscriminate and often unnecessary prescriptions.
Methods:
From pathology laboratory tests, six biochemical markers were selected and combined with microbiology outcomes from susceptibility tests to create a unique dataset with over one and a half million daily profiles to perform infection risk inference. Outliers were discarded using the inter-quartile range rule and several sampling techniques were studied to tackle the class imbalance problem. The first phase selects the most effective and robust model during training using ten-fold stratified cross-validation. The second phase evaluates the final model after isotonic calibration in scenarios with missing inputs and imbalanced class distributions.
Results:
More than 50% of infected profiles have daily requested laboratory tests for the six biochemical markers with very promising infection inference results: area under the receiver operating characteristic curve (0.80-0.83), sensitivity (0.64-0.75) and specificity (0.92-0.97). Standardization consistently outperforms normalization and sensitivity is enhanced by using the SMOTE sampling technique. Furthermore, models operated without noticeable loss in performance if at least four biomarkers were available.
Conclusion:
The selected biomarkers comprise enough information to perform infection risk inference with a high degree of confidence even in the presence of incomplete and imbalanced data. Since they are commonly available in hospitals, Clinical Decision Support Systems could benefit from these findings to assist clinicians in deciding whether or not to initiate antimicrobial therapy to improve prescription practices.
Insights
Biochemical markers can accurately predict infection risk, aiding clinical decisions on antimicrobial use. This helps combat antimicrobial resistance by preventing unnecessary prescriptions in hospitals.
Area of Science:
- Biomedical Informatics
- Clinical Pathology
- Infectious Disease Epidemiology
Background:
- Antimicrobial resistance (AMR) is a growing threat to public health, exacerbated by the overuse of antimicrobials in hospitals.
- Clinical Decision Support Systems (CDSSs) can improve antimicrobial prescribing but may lead to unnecessary prescriptions without proper disease assessment.
Purpose of the Study:
- To develop a reliable method for infection risk inference using routinely available laboratory data.
- To support clinicians in making informed decisions regarding antimicrobial therapy initiation.
Main Methods:
- Combined six biochemical markers with microbiology susceptibility test outcomes from over 1.5 million daily patient profiles.
- Employed outlier detection, sampling techniques (SMOTE), and ten-fold cross-validation for robust model training and evaluation.
- Assessed model performance under conditions of missing data and class imbalance.
Main Results:
- Achieved high accuracy in infection risk inference with an area under the ROC curve of 0.80-0.83, sensitivity of 0.64-0.75, and specificity of 0.92-0.97.
- Standardization proved more effective than normalization, and SMOTE improved sensitivity.
- Models maintained performance with as few as four available biomarkers.
Conclusions:
- Selected biochemical markers provide sufficient information for confident infection risk inference, even with incomplete or imbalanced data.
- These findings can enhance CDSSs, enabling better antimicrobial stewardship and reducing unnecessary prescriptions.
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