Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms
IN PROCESS. [1]
Contents
Abstract[1]
Background
Objective
This research is motivated by the issue of classifying illnesses of chronically ill patients for decision support in clinical settings. Our main objective is to propose multi-label classification of multivariate time series contained in medical records of chronically ill patients, by means of quantization methods, such as bag of words (BoW), and multi-label classification algorithms. Our second objective is to compare supervised dimensionality reduction techniques to state-of-the-art multi-label classification algorithms. The hypothesis is that kernel methods and locality preserving projections make such algorithms good candidates to study multi-label medical time series.
Study Design and Method
Results
Conclusion
Comments
Related Articles
Visualizing unstructured patient data for assessing diagnostic and therapeutic history
References
- ↑ 1.0 1.1 Bromuri S, Zufferey D, Hennebert J, Schumacher M. Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms. J Biomed Inform. 2014;51:165-75. http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pubmed/24879897