2011
Leiva-Murillo, Jose M; Artés-Rodríguez, Antonio; Baca-García, Enrique
Visualization and Prediction of Disease Interactions with Continuous-Time Hidden Markov Models Proceedings Article
En: NIPS 2011 Workshop on Personalized Medicine., Sierra Nevada, 2011.
Resumen | Enlaces | BibTeX | Etiquetas: Computational, Information-Theoretic Learning with Statistics, Theory & Algorithms
@inproceedings{Leiva-Murillo2011,
title = {Visualization and Prediction of Disease Interactions with Continuous-Time Hidden Markov Models},
author = {Jose M Leiva-Murillo and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},
url = {http://eprints.pascal-network.org/archive/00009110/},
year = {2011},
date = {2011-01-01},
booktitle = {NIPS 2011 Workshop on Personalized Medicine.},
address = {Sierra Nevada},
abstract = {This paper describes a method for discovering disease relationships and the evolution of diseases from medical records. The method makes use of continuous-time Markov chain models that overcome some drawbacks of the more widely used discrete-time chain models. The model addresses uncertainty in the diagnoses, possible diagnosis errors and the existence of multiple alternative diagnoses in the records. A set of experiments, performed on a dataset of psychiatric medical records, shows the capability of the model to visualize maps of comorbidity and causal interactions among diseases as well as to perform predictions of future evolution of diseases.},
keywords = {Computational, Information-Theoretic Learning with Statistics, Theory \& Algorithms},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper describes a method for discovering disease relationships and the evolution of diseases from medical records. The method makes use of continuous-time Markov chain models that overcome some drawbacks of the more widely used discrete-time chain models. The model addresses uncertainty in the diagnoses, possible diagnosis errors and the existence of multiple alternative diagnoses in the records. A set of experiments, performed on a dataset of psychiatric medical records, shows the capability of the model to visualize maps of comorbidity and causal interactions among diseases as well as to perform predictions of future evolution of diseases.