Clinical decision support system to assess the risk of sepsis using Tree Augmented Bayesian networks and electronic medical record data.
Health Informatics J
; 26(2): 841-861, 2020 06.
Article
en En
| MEDLINE
| ID: mdl-31195874
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Toma de Decisiones Asistida por Computador
/
Sepsis
/
Sistemas de Apoyo a Decisiones Clínicas
/
Registros Electrónicos de Salud
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Aspecto:
Determinantes_sociais_saude
Límite:
Humans
Idioma:
En
Revista:
Health Informatics J
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Reino Unido