Information Technology Reference
In-Depth Information
13
Medical Concept Representation and Data Mining
Mila Kwiatkowska and Najib T. Ayas
Abstract.
Medical data stored in clinical files and databases, such as patient his-
tories and medical records, as well as research data collected for various clinical
studies, are invaluable sources of medical knowledge. The computer-based data-
mining techniques provide a tremendous opportunity for discovering patterns, re-
lationships, trends, typical cases, and irregularities in these large volumes of data.
The patterns discovered from data can be used to stimulate further research, as well
as to create practical guidelines for diagnosis, prognosis, and treatment. Thus, a
successful data-mining process may result in a significant improvement in the qual-
ity and efficiency of both medical research and health care services. Many studies
have already demonstrated the practical values of data-mining techniques in various
fields. However, in contrast with more traditional areas of data mining, such as min-
ing of financial data or mining of purchasing records, medical data-mining presents
greater challenges. These challenges arise not only from the complexity of the med-
ical data, but more fundamentally from the difficulty of linking the medical data to
medical concepts or rather medical concepts to medical data. Thus, although com-
puterized medical equipment allows us to store increasingly large volumes of data,
the problem lies in defining the meaning of the data and even more so in defining
the medical concepts themselves.
This paper will address issues specific to medical data and medical data mining
in the context of Dr. Kazem Sadegh-Zadeh's discussion of the typology of medical
concepts. In his
Handbook of Analytic Philosophy of Medicine
, Dr. Sadegh-Zadeh
outlines four main classes of medical concepts: individual, qualitative (classifica-
tory), comparative, and quantitative. Moreover, he introduces a novel distinction
between classical and non-classical concepts. We will explain how his typology can
be utilized for conceptual modelling of medical data. Specifically we will illustrate
how this typology can pertain to data used in the diagnosis and treatment of sleep
disorders.
