Disease Prediction using Machine Learning, Deep Learning and Data Analytics

Mining Standardized EHR Data: Exploration, Issues, and Solution

Author(s): Shivani Batra*, Vinay Kumar, Neha Kohli and Vaishali Arya

Pp: 146-158 (13)

DOI: 10.2174/9789815179125124010015

* (Excluding Mailing and Handling)


Medical database is among the most crucial databases in terms of their applicability to human life. Many researchers are in search of knowledge that is abstracted within the data. Data mining is popular in today's world as it gives access to knowledge that is otherwise unavailable. The concealed knowledge which is offered as a result of it can help the individual to make better decisions. Data mining tools in health have great potential. These solutions may be divided into four categories: therapeutic efficacy assessment, patient care, customer service, and embezzlement monitoring. The authors discovered that giving decision assistance in the medical sector with an emphasis on electronic health records (EHRs) can save lives. Though offering decision assistance in EHRs using data mining is valuable, it needs consistency. As a result, the authors intend to use data mining methods on standardised EHRs to create a decision support system. This paper presents the state-of-the-art data mining approaches and their application in the healthcare sector. It provides an integrated summary and a comparison detail of the existing literature. This chapter surveys several issues that need to be handled before employing data mining on EHRs and further proposes a solution for dealing with these problems. The problems such as multiple origins, multiple formats, missing data, distinguished users, data granularity, flexibility, and sparseness need immediate attention from researchers. Resolving these problems is important to build an efficient standardized EHRs database.

Keywords: Data Mining, EAV Model, Health Data, Standardized EHRs, Sparseness.

Related Books
© 2024 Bentham Science Publishers | Privacy Policy