VISUALIZING INSURANCE CLAIMS DATA FOR CLINICAL EVENTS

Abstract

Despite the growing interest in the use of large amounts of clinical data in research and clinical practice, the mining of clinical events for the purpose of patient education remains understudied in clinical data mining and data visualization. Fp-Growth, Apriori, and Spade are three well-known data mining techniques, and this research aims to find out how well they perform in the context of crd patient education applications. After mining the data for sequence or association patterns, it is challenging to analyze them due to the increasing complexity of the underlying technology. In particular, patients have little opportunity to use data mined to improve their understanding of the development of their condition. For example, a timeline chart that displays a series of clinical events in chronological order and in a visual format can be used to create a presentation. Some timelines are organized on a single scale, while others present a series of different clinical events, such as diagnoses, operations, medications, or test findings.

Keywords

Insurance, Clinical Events, Fp-Growth, education applications, clinical events