| Description |
1 online resource (vii, 278 pages) |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
1. Introduction -- 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 -- 5. Types' internal structure and "non-constructive" ("NC4") type theory -- 6. Using code models to instantiate data models -- 7. Multi-aspect modules and image annotation -- 8. Image annotation as a multi-aspect case study -- 9. Conceptual spaces and scientific data models. |
| Summary |
"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."-- Provided by publisher. |
| Biography |
Amy Neustein, PhD is CEO and founder of Linguistic Technology Systems, Fort Lee, NJ. Nathaniel Christen is lead software architect at Linguistic Technology Systems. |
| Note |
Print version record. |
| Subject |
Medical care -- Data processing.
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Data integration (Computer science)
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COVID-19 (Disease) -- Treatment.
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Cancer -- Treatment.
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Cardiovascular system -- Diseases -- Treatment.
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Medical Informatics |
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COVID-19 -- therapy |
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Neoplasms -- therapy |
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Cardiovascular Diseases -- therapy |
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Intégration de données (Informatique)
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COVID-19 -- Traitement.
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Cancer -- Traitement.
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Appareil cardiovasculaire -- Maladies -- Traitement.
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Soins médicaux -- Informatique.
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Médecine -- Informatique.
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Cancer -- Treatment
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Cardiovascular system -- Diseases -- Treatment
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COVID-19 (Disease) -- Treatment
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Data integration (Computer science)
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Medical care -- Data processing
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| Added Author |
Christen, Nathaniel, author.
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| Other Form: |
Print version: NEUSTEIN, AMY. INNOVATIVE DATA INTEGRATION AND CONCEPTUAL SPACE MODELING FOR COVID, CANCER, AND CARDIAC CARE. ELSEVIER ACADEMIC PRESS, 2022 0323851975 (OCoLC)1263864990 |
| ISBN |
9780323853569 electronic book |
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0323853560 electronic book |
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9780323851978 paperback |
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0323851975 paperback |
| Standard No. |
AU@ 000070667777 |
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