Machine learning-based clinical decision support for infection risk prediction

Ting Feng; David P Noren; Chaitanya Kulkarni; Sara Mariani; Claire Zhao; Erina Ghosh; Dennis Swearingen; Joseph Frassica; Daniel McFarlane; Bryan Conroy

doi:10.3389/fmed.2023.1213411

Machine learning-based clinical decision support for infection risk prediction

Front Med (Lausanne). 2023 Dec 18:10:1213411. doi: 10.3389/fmed.2023.1213411. eCollection 2023.

Authors

Affiliations

¹ Philips Research North America, Cambridge, MA, United States.
² Philips Research Bangalore, Bengaluru, India.
³ Department of Medical Informatics, Banner Health, Phoenix, AZ, United States.
⁴ Department of Biomedical Informatics, University of Arizona College of Medicine, Phoenix, AZ, United States.
⁵ Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States.

Abstract

Background: Healthcare-associated infection (HAI) remains a significant risk for hospitalized patients and a challenging burden for the healthcare system. This study presents a clinical decision support tool that can be used in clinical workflows to proactively engage secondary assessments of pre-symptomatic and at-risk infection patients, thereby enabling earlier diagnosis and treatment.

Methods: This study applies machine learning, specifically ensemble-based boosted decision trees, on large retrospective hospital datasets to develop an infection risk score that predicts infection before obvious symptoms present. We extracted a stratified machine learning dataset of 36,782 healthcare-associated infection patients. The model leveraged vital signs, laboratory measurements and demographics to predict HAI before clinical suspicion, defined as the order of a microbiology test or administration of antibiotics.

Results: Our best performing infection risk model achieves a cross-validated AUC of 0.88 at 1 h before clinical suspicion and maintains an AUC >0.85 for 48 h before suspicion by aggregating information across demographics and a set of 163 vital signs and laboratory measurements. A second model trained on a reduced feature space comprising demographics and the 36 most frequently measured vital signs and laboratory measurements can still achieve an AUC of 0.86 at 1 h before clinical suspicion. These results compare favorably against using temperature alone and clinical rules such as the quick sequential organ failure assessment (qSOFA) score. Along with the performance results, we also provide an analysis of model interpretability via feature importance rankings.

Conclusion: The predictive model aggregates information from multiple physiological parameters such as vital signs and laboratory measurements to provide a continuous risk score of infection that can be deployed in hospitals to provide advance warning of patient deterioration.

Keywords: clinical decision support (CDS); healthcare-associated infection (HAI); machine learning; model interpretability; pre-symptomatic infection risk.

Grants and funding

This work is sponsored by the US Department of Defense (DoD), Defense Threat Reduction Agency (DTRA) under project CB10560. The funding body did not play a role in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.