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A Preparation Framework for EHR Data to Construct CBR Case-Base
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Author(s): Shaker El-Sappagh (Mansoura University, Egypt), Mohammed Mahfouz Elmogy (Faculty of Computers and Information, Mansoura University, Egypt), Alaa M. Riad (Mansoura University, Egypt), Hosam Zaghloul (Mansoura University, Egypt)and Farid A. Badria (Mansoura University, Egypt)
Copyright: 2017
Pages: 34
Source title:
Handbook of Research on Machine Learning Innovations and Trends
Source Author(s)/Editor(s): Aboul Ella Hassanien (Cairo University, Egypt)and Tarek Gaber (Suez Canal University, Egypt)
DOI: 10.4018/978-1-5225-2229-4.ch016
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Abstract
Diabetes mellitus diagnosis is an experience-based problem. Case-Based Reasoning (CBR) is the first choice for these problems. CBR depends on the quality of its case-base structure and contents; however, building a case-base is a challenge. Electronic Health Record (EHR) data can be used as a starting point for building case-bases, but it needs a set of preparation steps. This chapter proposes an EHR-based case-base preparation framework. It has three phases: data-preparation, coding, and fuzzification. The first two phases will be discussed in this chapter using a diabetes diagnosis dataset collected from EHRs of 60 patients. The result is the case-base knowledge. The first phase uses some machine-learning algorithms for case-base data preparation. For encoding phase, we propose and apply an encoding methodology based on SNOMED-CT. We will build an OWL2 ontology from collected SNOMED-CT concepts. A CBR prototype has been designed, and results show enhancements to the diagnosis accuracy.
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