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Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care (Paperback)
Loot Price: R3,565
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Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care (Paperback)
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Total price: R3,585
Discovery Miles: 35 850
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In recent years, scientific research and translation medicine have
placed increased emphasis on computational methodology and data
curation across many disciplines, both to advance underlying
science and to instantiate precision-medicine protocols in the lab
and in clinical practice. The nexus of concerns related to
oncology, cardiology, and virology (SARS-CoV-2) presents a
fortuitous context within which to examine the theory and practice
of biomedical data curation. Innovative Data Integration and
Conceptual Space Modeling for COVID, Cancer, and Cardiac Care
argues that a well-rounded approach to data modeling should
optimally embrace multiple perspectives inasmuch as data-modeling
is neither a purely formal nor a purely conceptual discipline, but
rather a hybrid of both. On the one hand, data models are designed
for use by computer software components, and are, consequently,
constrained by the mechanistic demands of software environments;
data modeling strategies must accept the formal rigors imposed by
unambiguous data-sharing and query-evaluation logic. In particular,
data models are not well-suited for software-level deployment if
such models do not translate seamlessly to clear strategies for
querying data and ensuring data integrity as information is moved
across multiple points. On the other hand, data modeling is,
likewise, constrained by human conceptual tendencies, because the
information which is managed by databases and data networks is
ultimately intended to be visualized/utilized by humans as the
end-user. Thus, at the intersection of both formal and humanistic
methodology, data modeling takes on elements of both
logico-mathematical frameworks (e.g., type systems and graph
theory) and conceptual/philosophical paradigms (e.g., linguistics
and cognitive science). The authors embrace this two-sided aspect
of data models by seeking non-reductionistic points of convergence
between formal and humanistic/conceptual viewpoints, and by
leveraging biomedical contexts (viz., COVID, Cancer, and Cardiac
Care) so as to provide motivating examples and case-studies in this
volume.
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