Affiliation:
1. Department of Life Science, Brunel University London, UK
2. Department of Computer Science, Brunel University London, UK
Abstract
Predicting complications associated with complex disease is a challenging task given imbalanced and highly correlated disease complications along with unmeasured or latent factors. To analyse the complications associated with complex disease, this article attempts to deal with complex imbalanced clinical data, whilst determining the influence of latent variables within causal networks generated from the observation. 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). First, it adopts a Time Series Bootstrapping approach to re-sample the rare complication class with a replacement with respect to the dynamics of disease progression. Then, a combination of the Induction Causation algorithm and Link Strength metric (which is called IC*LS approach) is applied on the bootstrapped data for incrementally identifying latent variables. The most highlighted contribution of this paper gained insight into the disease progression by interpreting the latent states (with respect to the associated distributions of 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.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science
Reference42 articles.
1. Diabetes, obesity and hypertension in urban and rural people of bedouin origin in the united arab emirates;Ali;The Journal of Tropical Medicine and Hygiene,1995
2. Big data technologies: New opportunities for diabetes management;Bellazzi;Journal of Diabetes Science and Technology,2015
3. Predictive data mining in clinical medicine: A focus on selected methods and applications;Bellazzi;Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,2011
4. R. Bellazzi, L. Sacchi and S. Concaro, Methods and tools for mining multivariate temporal data in clinical and biomedical applications, in: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2009, pp. 5629–5632.
5. Mosaic: Models and simulation techniques for discovering diabetes influence factors;Cancela;Transactions of Japanese Society for Medical and Biological Engineering,2013
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献