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

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.

• Provides an analysis of how conceptual spaces and related cognitive linguistic approaches can inspire programming and query-processing models
• Outlines the vital role that data modeling/curation has played in significant medical breakthroughs
• Presents readers with an overview of how information-management approaches intersect with precision medicine, providing case studies of data-modeling in concrete scientific practice
• Explores applications of image analysis and computer vision in the context of precision medicine
• Examines the role of technology in scientific publishing, replication studies, and dataset curation

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).

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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. Provides an analysis of how conceptual spaces and related cognitive linguistic approaches can inspire programming and query-processing models Outlines the vital role that data modeling/curation has played in significant medical breakthroughs Presents readers with an overview of how information-management approaches intersect with precision medicine, providing case studies of data-modeling in concrete scientific practice Explores applications of image analysis and computer vision in the context of precision medicine Examines the role of technology in scientific publishing, replication studies, and dataset curation