Identifying Prescription Patterns with a Topic Model of Diseases and Medications

Publication date: Available online 27 September 2017 Source:Journal of Biomedical Informatics Author(s): Sungrae Park, Doosup Choi, Minki Kim, Wonchul Cha, Chuhyun Kim, Il-Chul Moon 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...
Source: Journal of Biomedical Informatics - Category: Information Technology Source Type: research