الفهرس 
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Abstract
Electronic Medical Records (EMR) can be defined as an organized collection of electronic
health information about patients and populations. Electronic format facilitates
information sharing across different health care sites, and easies its embedding in
network-connected enterprise-wide information systems like medical Case Based
Reasoning systems (CBR).
The sustained and ubiquitous availability of high-quality Operable Clinical Cases (OCC) is
deemed as a bottleneck towards the incorporation of medical CBR systems in any real
life medical diagnostic environment. Procurement of CBR compliant cases is quite
challenging, as this requires medical experts to map their experiential knowledge to an
unfamiliar computational formalism.
Although there are many EMR sources (files – databases) over the internet and in
patient-care places, the presence of well-structured EMR files in the internet is a major
problem for researchers in medical informatics, and integrating different EMR databases
is also another challenge.
To overcome the above two problems in order to get EMR, two techniques are
proposed: The first one is an efficient technique to extract EMR from different
databases, it is based on retrieving different relationships between patients’ different
data tables (files) and automatically generates EMRs in XML format, then building frame
based medical cases to form a case repository that can be used in medical diagnostic
systems.
This technique has been applied on different structured databases, taken from DAR EL
FOAAD hospital sited in the 6th of October city and EL TAYSEER hospital sited in EL
Sharkia city. Generated EMR has been evaluated by medical experts based in these two
hospitals and they have confirmed that the generated EMR are accurate with
percentage 85 %. The second technique is an intelligent approach for knowledge transformation from
EMR to clinical cases. This technique is based on Case-Based Reasoning methodology.
CBR can be used in developing medical diagnostic systems. It targets to collect medical
documents from Internet (MEDLINE library), and convert collected EMR document to
clinical cases. It consists of two main phases: First phase is based on using Semantic
Similarity Retrieval Model (SSRM) in retrieving medical documents. SSRM overcomes
the semantics problem, and associates retrieved documents containing semantically
similar terms. It has been tested on OHSUMED (This test collection was created to assist
information retrieval research. It is a clinically-oriented MEDLINE subset, consisting of
348,566 references. The test collection was built as part of a study assessing the use of
MEDLINE by physicians in a clinical setting). All OHSUMED documents are indexed by
title, and symptom. Second phase is using retrieved medical documents to generate
medical cases by mapping these EMR document data to defined cases attributes. Finally
building a case base repository that would be utilized in a CBR-medical diagnostic
system.
The experiment results (30 documents) had been done using Vector Space Model (VSM)
and SSRM with expansion with very similar terms (T = 0.9, T = 0.7, and T = 0.5). For
larger answer sets, SSRM with expansion threshold T = 0.5 found to be the best method.
For short answer sets, SSRM with expansion threshold T = 0.5 is the best method. An
explanation may be that it introduced many new terms and not all of them are
conceptually similar with the original query terms.
Generated cases have been manual validated by domain experts based on (El Tayseer
Hospitals) in the following domains (blood tests, general Surgery) and reported that 80
% of generated cases are accurate. They also reported that the rest percentage 20 % of
generated cases have missed a lot of data and there are repetitive values that make
these cases incorrect.