Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Computer Sciences
Abstract
Health social media offer useful data for patients and doctors concerning both various medicines and treatments. Usually, these data are accompanied by their assessments in 5- star scale. But such a detail classification has small usefulness because patients and doctors, first of all, want to know about negative cases and to study in detail the extreme ones. In the paper we build classifiers of texts just for these cases using combined classes as negative, all others and worst, satisfactory, best. For this, we study possibilities of different GMDH-based algorithms and compare them with the results of other methods. The selection of GMDH is provoked by two circumstances: (a) health social media contain significant informative noise, and (b) GMDH is essentially noise-immunity method. The experimental material is the popular health social network Askapatient.
DOI
https://doi.org/10.1109/STC-CSIT.2018.8526655
Recommended Citation
Akhtyamova, L., Alexandrov, M., Cardiff, J., Koshulko, O.: Building Classifiers with GMDH for Health Social Networks (DB AskaPatient). In:,i> Proc. of the Intern. Workshop on Inductive Modelling (IWIM-2018), IEEE, 2018 DOI:10.1109/STC-CSIT.2018.8526655
Publication Details
2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT)