Bayesian network models with decision tree analysis for management of childhood malaria in Malawi

BMC Medical Informatics and Decision Making

Gerald P. Douglas¹  Marian G. Michaels²  Marek J. Druzdzel³  Sanya B. Taneja⁴  Shyam Visweswaran⁵  Gregory F. Cooper⁵ 

[1] Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA;Global Health Informatics Institute, Area 3, Lilongwe, Malawi;Division of Infectious Diseases, Department of Pediatrics, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA;Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351, Bialystok, Poland;Intelligent Systems Program, University of Pittsburgh, 5108 Sennott Square, 210 South Bouquet Street, 15260, Pittsburgh, PA, USA;Intelligent Systems Program, University of Pittsburgh, 5108 Sennott Square, 210 South Bouquet Street, 15260, Pittsburgh, PA, USA;Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA;

关键词: Bayesian network model;    Decision tree;    Clinical decision support;    Childhood malaria;    Malawi;   

DOI  :  10.1186/s12911-021-01514-w

来源: Springer

【 摘 要 】

BackgroundMalaria is a major cause of death in children under five years old in low- and middle-income countries such as Malawi. Accurate diagnosis and management of malaria can help reduce the global burden of childhood morbidity and mortality. Trained healthcare workers in rural health centers manage malaria with limited supplies of malarial diagnostic tests and drugs for treatment. A clinical decision support system that integrates predictive models to provide an accurate prediction of malaria based on clinical features could aid healthcare workers in the judicious use of testing and treatment. We developed Bayesian network (BN) models to predict the probability of malaria from clinical features and an illustrative decision tree to model the decision to use or not use a malaria rapid diagnostic test (mRDT).MethodsWe developed two BN models to predict malaria from a dataset of outpatient encounters of children in Malawi. The first BN model was created manually with expert knowledge, and the second model was derived using an automated method. The performance of the BN models was compared to other statistical models on a range of performance metrics at multiple thresholds. We developed a decision tree that integrates predictions with the costs of mRDT and a course of recommended treatment.ResultsThe manually created BN model achieved an area under the ROC curve (AUC) equal to 0.60 which was statistically significantly higher than the other models. At the optimal threshold for classification, the manual BN model had sensitivity and specificity of 0.74 and 0.42 respectively, and the automated BN model had sensitivity and specificity of 0.45 and 0.68 respectively. The balanced accuracy values were similar across all the models. Sensitivity analysis of the decision tree showed that for values of probability of malaria below 0.04 and above 0.40, the preferred decision that minimizes expected costs is not to perform mRDT.ConclusionIn resource-constrained settings, judicious use of mRDT is important. Predictive models in combination with decision analysis can provide personalized guidance on when to use mRDT in the management of childhood malaria. BN models can be efficiently derived from data to support clinical decision making.

【 授权许可】

CC BY