Medication Episode Construction Framework for Retrospective Database Analyses of Patients With Chronic Diseases

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
$\pm \epsilon$
) is used to define the consecutive prescription relations considering the patients’ behavior. For example, patients occasionally arrive earlier or later than their appointment.
Results
:
$\epsilon$
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
$\epsilon$
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
$\epsilon$
. Furthermore, the results show that by using
$\epsilon$
, 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
$\epsilon$
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
$\epsilon$
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.