What is it about?
To analyse the complications associated with complex diseases, this article attempts to deal with complex imbalanced clinical data, whilst determining the influence of latent variables within causal networks generated from the observation.
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Why is it important?
Predicting complications associated with the complex disease is a challenging task given imbalanced and highly correlated disease complications along with unmeasured or latent factors.
Perspectives
This work proposes appropriate Intelligent Data Analysis methods for building Dynamic Bayesian networks with latent variables, applied to small-sized clinical data (a case of Type 2 Diabetes complications). An exploration of inference methods along with confidence interval assessed the influences of these latent variables. The obtained results demonstrated an improvement in the prediction performance.
Leila Yousefi
Read the Original
This page is a summary of: Identifying latent variables in Dynamic Bayesian Networks with bootstrapping applied to Type 2 Diabetes complication prediction, Intelligent Data Analysis, March 2022, IOS Press,
DOI: 10.3233/ida-205570.
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