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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.
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