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Data Mining and Knowledge Discovery

Erschienen in:

03.05.2021

Affinity analysis for studying physicians’ prescription behavior.

The case of hypertension and hyperlipidemia

verfasst von: Iraklis Varlamis

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 4/2021

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Abstract

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.

Vorheriger Artikel Adversarial balancing-based representation learning for causal effect inference with observational data

Nächster Artikel Feature extraction from unequal length heterogeneous EHR time series via dynamic time warping and tensor decomposition

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Titel: Affinity analysis for studying physicians’ prescription behavior.
The case of hypertension and hyperlipidemia
verfasst von: Iraklis Varlamis
Publikationsdatum: 03.05.2021
Verlag: Springer US
Erschienen in: Data Mining and Knowledge Discovery / Ausgabe 4/2021
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI: https://doi.org/10.1007/s10618-021-00758-4

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