Clinical decision support system to assess the risk of sepsis using Tree Augmented Bayesian networks and electronic medical record data. (June 2020)

• Record Type: Journal Article
• Authors: Gupta, Akash; Liu, Tieming; Shepherd, Scott
• Other Names: Bian Jiang guest-editor.; Zhang Yaoyun guest-editor.; Tao Cui guest-editor.
• Is Part Of: Health informatics journal. Volume 26:Number 2(2020)
• Page Start: 841
• Page End: 861
• Publication Date: 2020-06
• Subjects: artificial intelligence -- Bayesian classifiers -- clinical decision support system -- data mining -- machine learning -- sepsis; Medical informatics -- Periodicals; 610.285
• DOI: 10.1177/1460458219852872
• Languages: English
• ISSNs: 1460-4582

Abstract

Early and accurate diagnoses of sepsis enable practitioners to take timely preventive actions. The existing diagnostic criteria suffer from deficiencies, such as triggering false alarms or leaving conditions undiagnosed. This study aims to develop a clinical decision support system to predict the risk of sepsis using tree augmented naive Bayesian network by identifying the optimal set of biomarkers. The key feature of our approach is that we captured the dynamics among biomarkers. With an area under receiver operating characteristic of 0.84, the proposed model outperformed the competing diagnostic criteria (systemic inflammatory response syndrome = 0.59, quick sepsis-related organ failure assessment = 0.65, modified early warning system = 0.75, sepsis-related organ failure assessment = 0.80). The richness of our proposed model is measured not only by achieving high accuracy, but also by utilizing fewer biomarkers. We also propose a left-center-right imputation method suitable for electronic medical record data. This method uses the individual patient's visit, instead of aggregated (mean or median) value, to impute the missing data.