Abstract
Objective: For chronic diseases, medical history reconstruction is essential for retrospective database analyses. One important aspect is determining which prescriptions belong to the same episode. However, a standard framework for this task is still lacking, particularly for multitherapy datasets. This paper presents a medication episode construction framework for the medical history of patients with chronic diseases. Methods : Allen's relaxed temporal relations (i.e., temporal relations with time constraints relaxed by) is used to define the consecutive prescription relations considering the patients' behavior. For example, patients occasionally arrive earlier or later than their appointment. Results: influences the generation of stable periods (i.e., periods of time, at least three months, in which a medication is continuously taken by a patient). When using the lowest selected value (7 days), considerably fewer shorter stable periods (for durations less than 300 days) are produced and more longer stable periods are produced compared to cases without using. Furthermore, the results show that by using , regarding the number of events, where a stable period continues the previous stable period, decreases and the number of medication transition events available to be observed increases. Conclusion : Using in medication episode construction from multitherapy prescription datasets enables the longer expression of short-duration fragmented prescriptions and pruning repetitive prescriptions. Significance: Our proposed framework is designed for multitherapy datasets, which has not been addressed by previous studies. The concept of relaxes the prescription relation against noise caused by the patient behavior and consequently provides a compact, but informative search space for observing medication transition events in a longitudinal analysis.
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Khotimah, P. H., Sugiyama, Y., Yoshikawa, M., Hamasaki, A., Sugiyama, O., Okamoto, K., & Kuroda, T. (2018). Medication Episode Construction Framework for Retrospective Database Analyses of Patients with Chronic Diseases. IEEE Journal of Biomedical and Health Informatics, 22(6), 1949–1959. https://doi.org/10.1109/JBHI.2017.2786741
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