Data-driven approach for bio-medical and healthcare/ Nilanjan Dey, editor.
Dey, Nilanjan, 1984- editor.
2023
R858
Available Online

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Title Data-driven approach for bio-medical and healthcare/ Nilanjan Dey, editor.
ISBN 9789811951848 (electronic bk.)
9811951845 (electronic bk.)
9811951837
9789811951831
DOI https://doi.org/10.1007/978-981-19-5184-8
Publication Details Singapore : Springer, [2023]
Language English
Description 1 online resource.
Item Number 10.1007/978-981-19-5184-8 doi
Call Number R858
Dewey Decimal Classification 610.285
Summary The book presents current research advances, both academic and industrial, in machine learning, artificial intelligence, and data analytics for biomedical and healthcare applications. The book deals with key challenges associated with biomedical data analysis including higher dimensions, class imbalances, smaller database sizes, etc. It also highlights development of novel pattern recognition and machine learning methods specific to medical and genomic data, which is extremely necessary but highly challenging. The book will be useful for healthcare professionals who have access to interesting data sources but lack the expertise to use data mining effectively.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed November 3, 2022).
Added Author Dey, Nilanjan, 1984- editor.
Series Database professional's library.
Available in Other Form Print version: 9789811951831
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Chapter 1. Personal Health Record Data-Driven Integration of Heterogeneous Data
Chapter 2. Privacy issues in data-driven healthcare
Chapter 3. Personalizing the Patient Discharge Process and Follow Up Using Machine Learning Algorithms, Assessment Questionnaires and Ontology Reasoning
Chapter 4. Explaining decisions of quantum algorithm: patient specific features explanation for epilepsy disease
Chapter 5. Bioinformatics study for determination of the binding efficacy of heme-based protein
Chapter 6. Growth Trend of Swine Flu and Covid 19 Pandemic Affected Patients using Fuzzy Cellular Automata: A Study
Chapter 7. Data-driven approach study for the prediction and detection of infectious disease outbreak
Chapter 8. Design and development of interactive, real time dashboard to understand COVID-19 situation in Pune
Chapter 9. Analyzing The Impact of Covid-19 and Vaccination using Machine Learning and ANN
Chapter 10. Development of Psychiatric COVID-19 CHATBOT using Deep Learning
Chapter 11. Adv nced Mathematical Model to Measure the Severity of any Pandemics
Chapter 12. Semi-Structured Patient Data in Electronic Health Record.

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