Medication adherence supporting model based on markov logic network using tuberculosis patients data

In this paper, we propose a patient medication adherence supporting system to support continuous patient medication for increasing the rate of treatment for tuberculosis. The proposed model is based on the Markov Logic Network which is one of statistical relational learning approach using doctor’s decision rule. The Markov logic network learns the relationships of data fields from tuberculosis patients. The proposed model infers medication adherence group using the newly given patient data via the trained Markov logic network.