Affinity analysis for studying physicians’ prescription behavior: The case of hypertension and hyperlipidemia

• Published in: Data mining and knowledge discovery 2021-07, Vol.35 (4), p.1739-1759
• Main Author: Varlamis, Iraklis
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Information Storage and Retrieval; Physics; Special Issue: Mining for Health; Statistics for Engineering
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-021-00758-4

The current study refers to the affinity analysis of a prescription and diagnosis dataset and focuses on how prescription behavior changes after the detection of a disorder. Using a subset of patients that have been diagnosed, at least once, with hypertension and hyperlipidemia, examines the prescription behavior of the doctor after the detection of gastroesophageal reflux (K21), or insulin-dependent diabetes mellitus (E10). Diagnosis and prescription data were collected during consecutive visits of 4473 patients in a 3 years period. The analysis of the prescription data before and after the diagnosis of K21 and E10 reveals the popular substances for each disorder, such as Metformin for E10, or Omeprazole for K21 and substances that are discontinued including the same popular substances as well. It also reveals that substances that treat the main disorders of the group, such as Simvastatin and Hydrochlorothiazide are discontinued after the diagnosis of K21 and E10. Apart from the medical findings, which must be subject of further research, the proposed methodology can be employed for studying the prescription behavior of physicians and how it changes after specific diagnoses.