Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care
Published on 24. January 2022
Book
Paperback/Softback
286 pages
978-0-323-85197-8 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
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.
Product notice
Paperback (trade)
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 15 mm
Weight
501 gr
ISBN-13
978-0-323-85197-8 (9780323851978)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Amy Neustein | Nathaniel Christen
Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care
E-Book
01/2022
Academic Press
€148.00
Available for download
Persons
Amy Neustein, PhD is CEO and Founder of Linguistic Technology Systems, in Fort Lee, NJ (USA), a think tank for database engineering, scientific computing, and programming language theory. She is the Volume Editor of 'Advances in Ubiquitous Computing: Cyber-Physical Systems, Smart Cities, and Ecological Monitoring' (Elsevier 2020) and author/editor of 15 academic books covering a wide range of topics: speech technology, natural language processing, robotics in healthcare, mobile speech, text mining, voice technologies for speech reconstruction and enhancement, signal and acoustic modeling for speech and communication disorders, acoustic analysis of pathologies in infants and children, legal jurisprudence and child health-related issues, forensic speaker recognition, and AI, IoT, Big Data, and Cloud Computing for Industry 4.0. She has authored over 75 articles/chapters/conference papers on this wide panoply of subjects. Dr. Neustein received her PhD in Sociology (with a concentration in sociolinguistics and ethnomethodology) from Boston University. She has served as Editor-in-Chief of the 'International Journal of Speech Technology' (Springer) from 2008 till present. She was featured in March 2018 in the SpringerNature "Women in STEM? joint campaign with the United Nations for women in science and technology during Women's History Month. Dr. Neustein serves as Series Editor of 'SpringerBriefs in Speech Technology: Studies in Signal Processing, Natural Language Understanding, and Machine Learning' (Springer); Series Editor of two additional book series: 'Signals and Communication Technology' (Springer); 'Speech Technology and Text Mining in Medicine and Healthcare' (de Gruyter). 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.
Content
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
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