Electronic Health Record (EHR) creates a huge number of databases which are
being updated dynamically. Major goal of interoperability in healthcare is to
facilitate the seamless exchange of healthcare related data and an environment
to supports interoperability and secure transfer of data. The health care
organisations face difficulties in exchanging patient’s health care information
and laboratory reports etc. due to a lack of semantic interoperability. Hence,
there is a need of semantic web technologies for addressing healthcare
interoperability problems by enabling various healthcare standards from various
healthcare entities (doctors, clinics, hospitals etc.) to exchange data and its
semantics which can be understood by both machines and humans. Thus, a
framework with a similarity analyser has been proposed in the thesis that dealt
with semantic interoperability. While dealing with semantic interoperability,
another consideration was the use of word embedding and ontology for
knowledge discovery. In medical domain, the main challenge for medical
information extraction system is to find the required information by considering
explicit and implicit clinical context with high degree of precision and accuracy.
For semantic similarity of medical text at different levels (conceptual, sentence
and document level), different methods and techniques have been widely
presented, but I made sure that the semantic content of a text that is presented
includes the correct meaning of words and sentences. A comparative analysis
of approaches included ontology followed by word embedding or vice-versa
have been applied to explore the methodology to define which approach gives
better results for gaining higher semantic similarity. Selecting the Kidney Cancer
dataset as a use case, I concluded that both approaches work better in different circumstances. However, the approach in which ontology is followed by word
embedding to enrich data first has shown better results. Apart from enriching
the EHR, extracting relevant information is also challenging. To solve this
challenge, the concept of analogy has been applied to explain similarities
between two different contents as analogies play a significant role in
understanding new concepts. The concept of analogy helps healthcare
professionals to communicate with patients effectively and help them
understand their disease and treatment. So, I utilised analogies in this thesis to
support the extraction of relevant information from the medical text. Since
accessing EHR has been challenging, tweets text is used as an alternative for
EHR as social media has appeared as a relevant data source in recent years.
An algorithm has been proposed to analyse medical tweets based on analogous
words. The results have been used to validate the proposed methods. Two
experts from medical domain have given their views on the proposed methods
in comparison with the similar method named as SemDeep. The quantitative
and qualitative results have shown that the proposed analogy-based method
bring diversity and are helpful in analysing the specific disease or in text
classification.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19750 |
| Date | January 2021 |
| Creators | Naveed, Arjmand |
| Contributors | Thakker, Dhaval, Hu, Yim Fun, Mishra, Bhupesh K. |
| Publisher | University of Bradford, Faculty of Engineering and Informatics. School of Electrical Engineering and Computer Science |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
Page generated in 0.002 seconds