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Conference Paper Medication Adherence Supporting Model based on Markov Logic Network using Tuberculosis Patients Data
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Authors
Bumhwi Kim, Kee-Koo Kwon, Kyu Hyung Kim, Eunchang Choi, Jae-Wook Nah
Issue Date
2018-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.66-68
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539524
Abstract
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.
KSP Keywords
Data field, Decision rules, Learning approach, Markov logic network, Patient data, Proposed model, Statistical relational learning, Supporting system, medication adherence, model-based