Disease-medicine topic model for prescription record mining

Analyzing patient records is important for improving the quality of medical services and for understanding each patient's historical diseases. However, the huge size of the data requires statistical analysis procedures. In this paper, we proposed a probabilistic model-the disease-medicine topic...

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Bibliographic Details
Published in2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC) pp. 86 - 93
Main Authors Sungrae Park, Doosup Choi, Wonsung Lee, Jung, Dain, Minki Kim, Il-Chul Moon
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2014
Subjects
Adaptation models
Analytical models
Biological system modeling
Coherence
Data mining
Diseases
information retrivial
machine learning
Medical diagnostic imaging
medical mining
text mining
topic modeling
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Summary:Analyzing patient records is important for improving the quality of medical services and for understanding each patient's historical diseases. However, the huge size of the data requires statistical analysis procedures. In this paper, we proposed a probabilistic model-the disease-medicine topic model (DMTM)-to explore connected knowledge about diseases and medicines. In the model, diseases and medicines are modeled using generative process. We used the latent Dirichlet allocation, which is one of the most popular topic models, as the baseline model. Then, we compared the qualities of topic representations quantitatively and qualitatively. The comparison results showed that the topics derived from the DMTM are clearer to identify and that specific patterns were found in the diseases and medicines. In the case of topic network analysis, these specific patterns were proved using centrality measurements.
ISSN:1062-922X
2577-1655
DOI:10.1109/SMC.2014.6973889