Flexible learning tree augmented naïve classifier and its application

Ren, Hongjia; Guo, Qiulin

doi:10.1016/j.knosys.2022.110140

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…TAN relies on probability and graph theory to perform classification, which labels an outcome based on the provided evidence. Along with other Bayesian classifiers such as Naïve Bayes (NB), TAN is commonly used in medical diagnosis, pattern recognition, and natural language processing (7,25,26). Appendix 1 and Figure S2 show the simplified example of TAN and NB for sepsis diagnosis and the algorithm to construct TAN (27).…”

Section: Tree Augmented Naïve Bayesmentioning

confidence: 99%

Effective diagnosis of sepsis in critically ill children using probabilistic graphical model

Nguyen¹,

Poh²,

Chong³

et al. 2023

Transl Pediatr

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Background: Probabilistic graphical model, a rich graphical framework in modelling associations between variables in complex domains, can be utilized to aid clinical diagnosis. However, its application in pediatric sepsis remains limited. This study aims to explore the utility of probabilistic graphical models in pediatric sepsis in the pediatric intensive care unit. Methods:We conducted a retrospective study on children using the first 24-hour clinical data of the intensive care unit admission from the Pediatric Intensive Care Dataset, 2010-2019. A probabilistic graphical model method, Tree Augmented Naive Bayes, was used to build diagnosis models using combinations of four categories: vital signs, clinical symptoms, laboratory, and microbiological tests. Variables were reviewed and selected by clinicians. Sepsis cases were identified with the discharged diagnosis of sepsis or suspected infection with the systemic inflammatory response syndrome. Performance was measured by the average sensitivity, specificity, accuracy, and area under the curve of ten-fold cross-validations.Results: We extracted 3,014 admissions [median age of 1.13 (interquartile range: 0.15-4.30) years old].There were 134 (4.4%) and 2,880 (95.6%) sepsis and non-sepsis patients, respectively. All diagnosis models had high accuracy (0.92-0.96), specificity (0.95-0.99), and area under the curve (0.77-0.87). Sensitivity varied with different combinations of variables. The model that combined all four categories yielded the best performance [accuracy: 0.93 (95% confidence interval: 0.916-0.936); sensitivity: 0.46 (95% confidence interval: 0.376-0.550), specificity: 0.95 (95% confidence interval: 0.940-0.956), area under the curve: 0.87 (95% confidence interval: 0.826-0.906)]. Microbiological tests had low sensitivity (<0.10) with high incidence of negative results (67.2%). Conclusions:We demonstrated that the probabilistic graphical model is a feasible diagnostic tool for pediatric sepsis. Future studies using different datasets should be conducted to assess its utility to aid clinicians in the diagnosis of sepsis.

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