Trajectory Analysis of Laboratory Tests as Medical Complex Data Mining
Abstract
Finding temporally covariant variables is very important for clinical practice because we are able to obtain the measurements of some examinations very easily, while it takes a long time for us to measure other ones. Also, unexpected covariant patterns give us new knowledge for temporal evolution of chronic diseases. This paper focuses on clustering of trajectories of temporal sequences of two laboratory examinations. First, we map a set of time series containing different types of laboratory tests into directed trajectories representing temporal change in patients’ status. Then the trajectories for individual patients are compared in multiscale and grouped into similar cases by using clustering methods. Experimental results on the chronic hepatitis data demonstrated that the method could find the groups of trajectories which reflects temporal covariance of platelet, albumin and choline esterase.
Cite
Text
Hirano and Tsumoto. "Trajectory Analysis of Laboratory Tests as Medical Complex Data Mining." European Conference on Machine Learning, 2007. doi:10.1007/978-3-540-68416-9_3Markdown
[Hirano and Tsumoto. "Trajectory Analysis of Laboratory Tests as Medical Complex Data Mining." European Conference on Machine Learning, 2007.](https://mlanthology.org/ecmlpkdd/2007/hirano2007ecml-trajectory/) doi:10.1007/978-3-540-68416-9_3BibTeX
@inproceedings{hirano2007ecml-trajectory,
title = {{Trajectory Analysis of Laboratory Tests as Medical Complex Data Mining}},
author = {Hirano, Shoji and Tsumoto, Shusaku},
booktitle = {European Conference on Machine Learning},
year = {2007},
pages = {27-41},
doi = {10.1007/978-3-540-68416-9_3},
url = {https://mlanthology.org/ecmlpkdd/2007/hirano2007ecml-trajectory/}
}