Identifying Prescription Patterns with a Topic Model of Diseases and Medications
- categorize
- Machine Learning
- Month
- Nov
- Journal Name
- Journal of Biomedical Informatics
- Volume
- 75
- Page
- 35–47
- File
- 1-s2.0-S1532046417302009-main.pdf (3.2M) 41회 다운로드 DATE : 2023-11-09 22:15:57
Park, S., Choi, D. S., Kim, M., Chul, W., Kim, C. H., & Moon, I. C.(2017). Identifying prescription patterns with a topic model of diseases and medications. Journal of Biomedical Informatics, 75, 35–47
Abstract
Wide variance exists among individuals and institutions for treating patients with medicine. This paper analyzes prescription patterns using a topic model with more than four million prescriptions. Specifically, we propose the disease-medicine pattern model (DMPM) to extract patterns from a large collection of insurance data by considering disease codes joined with prescribed medicines. We analyzed insurance prescription data from 2011 with DMPM and found prescription patterns that could not be identified by traditional simple disease classification, such as the International Classification of Diseases (ICD). We analyzed the identified prescription patterns from multiple aspects. First, we found that our model better explain unseen prescriptions than other probabilistic models. Second, we analyzed the similarities of the extracted patterns to identify their characteristics. Third, we compared the identified patterns from DMPM to the known disease categorization, ICD. This comparison showed what additional information can be provided by the data-oriented bottom-up patterns in contrast to the knowledge-based top-down categorization. The comparison results showed that the bottom-up categorization allowed for the identification of (1) diverse treatment options for the same disease symptoms, and (2) diverse disease cases sharing the same prescription options. Additionally, the extracted bottom-up patterns revealed treatment differences based on basic patient information better than the top-down categorization. We conclude that this data-oriented analysis will be an effective alternative method for analyzing the complex interwoven disease-prescription relationship.
@article{park-2017,
author = {Park, Sungrae and Choi, Doo Sup and Kim, Minki and Chul, Won and Kim, Chu Hyun and Moon, Il‐Chul},
journal = {Journal of Biomedical Informatics},
month = {11},
pages = {35--47},
title = {{Identifying prescription patterns with a topic model of diseases and medications}},
volume = {75},
year = {2017},
doi = {10.1016/j.jbi.2017.09.003},
}
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