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

Scientific research and translation medicine have placed increased emphasis on computational methodology and 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 embrace multiple perspectives, as it is a hybrid of both formal and conceptual disciplines. Data models are designed for use by computer software components and are constrained by the mechanistic demands of software environments, while also being constrained by human conceptual tendencies. The authors embrace this two-sided aspect of data models by seeking non-reductionistic points of convergence between formal and humanistic/conceptual viewpoints and leveraging biomedical contexts to provide motivating examples and case-studies.

Format: Paperback / softback
Length: 286 pages
Publication date: 24 January 2022
Publisher: Elsevier Science & Technology

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 field of data modeling in the context of biomedical research has evolved significantly in recent years. While it was once primarily focused on the technical aspects of representing and manipulating data, it has now expanded to encompass a broader range of perspectives and methodologies. One important aspect of this evolution is the recognition that data modeling is not a purely formal or conceptual discipline, but rather a hybrid of both. On the one hand, data models are designed to be used by computer software components, which means that they must adhere to strict technical specifications and constraints. This includes the use of formal languages such as SQL and XML to define data structures and relationships, as well as the use of algorithms and data structures to optimize data storage and retrieval. On the other hand, data modeling is also constrained by human conceptual tendencies. The information that is managed by databases and data networks is ultimately intended to be visualized and utilized by humans, which means that data models must be designed with the end-user in mind. This requires a deep understanding of the domain and the specific needs of the end-users, as well as the ability to translate complex data concepts into intuitive and accessible representations.

One approach to addressing these challenges is to adopt a well-rounded approach to data modeling. This approach should ideally embrace multiple perspectives and methodologies, including both formal and conceptual approaches. Formal approaches to data modeling involve the use of mathematical and logical frameworks to define data structures and relationships. These approaches are often used in fields such as computer science and engineering, where the emphasis is on efficiency and accuracy. They involve the use of formal languages such as SQL and XML to define data structures and relationships, as well as the use of algorithms and data structures to optimize data storage and retrieval.

In contrast, conceptual approaches to data modeling involve the use of human-centered design principles to define data structures and relationships. These approaches are often used in fields such as healthcare and social sciences, where the emphasis is on usability and accessibility. They involve the use of visual representations and metaphors to convey complex data concepts, as well as the use of storytelling and narrative techniques to engage end-users.

Another important aspect of a well-rounded approach to data modeling is the use of innovative data integration and conceptual space modeling techniques. These techniques involve the use of advanced computational methods to integrate and analyze large and complex datasets, as well as the use of visualization and interactive tools to explore and interpret the data. They can help researchers to identify patterns and trends.

In conclusion, the field of data modeling in the context of biomedical research has evolved significantly in recent years. While it was once primarily focused on the technical aspects of representing and manipulating data, it has now expanded to encompass a broader range of perspectives and methodologies. A well-rounded approach to data modeling should ideally embrace multiple perspectives and methodologies, including both formal and conceptual approaches. This approach should also incorporate innovative data integration and conceptual space modeling techniques to help researchers to identify patterns and insights in complex datasets. By doing so, researchers can better understand the underlying science and develop more effective precision-medicine protocols in the lab and in clinical practice.

Weight: 558g
Dimension: 237 x 372 x 25 (mm)
ISBN-13: 9780323851978