Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care

Erscheinungsjahr 2022
ISBN: 978-0-323-85197-8
Verlag: Elsevier Science & Technology

Buch, Englisch, 286 Seiten, Format (B × H): 372 mm x 237 mm, Gewicht: 558 g

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.

Zielgruppe

<p>Academics (scientists, researchers, MSc, and/or PhD students) from the fields of Biomedical Engineering, Computer Science, Biology, and Information Technology. The audience also includes epidemiologists, computational epidemiologists, software engineers, biostatisticians, and data scientists.</p>

Autoren/Hrsg.

Neustein, Amy

Christen, Nathaniel

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik

Inhaltsverzeichnis

1. Introduction Part I: Biomedical data formats and data integration 2. Data structures associated with biomedical research 3. Data mining and predictive analytics for cancer and COVID-19 4. Modular design, image biomarkers, and radiomics Part II: Type theory, graphs, and conceptual spaces 5. Types' internal structure and "non-constructive ("NC4�) type theory 6. Using code models to instantiate data models Part III: Conceptual spaces and graph-oriented data-modeling paradigms 7. Multi-aspect modules and image annotation 8. Image annotation as a multi-aspect case study 9. Conceptual spaces and scientific data models

Über Autor(innen)

Christen, Nathaniel
Nathaniel Christen is Lead Software Architect at Linguistic Technology Systems, in Fort Lee, NJ (USA). He received a BA in Mathematics, with a concentration in theoretical computer science, from Simon's Rock of Bard College in Massachusetts. He later completed his Masters in Cultural Studies at George Mason University in Virginia. He is a doctoral candidate (on leave) at the University of Ottawa in Canada. His doctoral research and dissertation have focused on the interrelation of phenomenology, cognitive linguistics, and the philosophy of science. Mr. Christen has served as a technology advisor for a student/faculty project on the implementation of a document repository serving as a digital archive for the emerging publications of students and faculty members. He served as a Teacher's Assistant at the University of Ottawa, where he taught classes in logic, ethics, and Kantian philosophy. He has programmed extensively in C++, Lisp, and custom languages. He contributed a lengthy chapter on hypergraph-based type theory for software development in a cyber-physical context to Elsevier's 'Advances in Ubiquitous Computing', edited by Amy Neustein.