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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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Published on: December 18, 2016

Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level

Syed Hasib Akhter Faruqui¹, Adel Alaeddini¹, Carlos A Jaramillo²

¹Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, United States of America.

|July 13, 2018

Summary

This study introduces an unsupervised Bayesian network to model the emergence of multiple chronic conditions. The novel approach accurately predicts comorbidity development, comparable to supervised methods.

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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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Published on: August 5, 2014

Area of Science:

Computational epidemiology
Health informatics
Network science

Background:

The emergence of multiple chronic conditions is a growing clinical concern.
Understanding the temporal relationships between comorbidities and risk factors is crucial but complex.

Purpose of the Study:

To propose an unsupervised multi-level temporal Bayesian network for modeling the emergence of multiple chronic conditions.
To represent relationships among comorbidities and patient risk factors over time.

Main Methods:

Utilized an unsupervised multi-level temporal Bayesian network.
Employed maximum weight spanning tree, greedy search, and longest path algorithms.
Analyzed a de-identified dataset of over 250,000 patients from the U.S. Department of Veterans Affairs.

Main Results:

The unsupervised Bayesian network demonstrated accurate predictive performance.
Performance was comparable to top-performing semi-supervised and supervised learning approaches.
Outperformed multivariate probit regression, multinomial logistic regression, and latent regression Markov mixture clustering.

Conclusions:

Unsupervised learning offers a viable and effective approach for modeling complex comorbidity emergence.
The proposed method provides insights into the temporal dynamics of chronic conditions.
This approach can aid in understanding and potentially intervening in the development of multiple chronic conditions.