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Data-driven approach for bio-medical...

Dey, Nilanjan.

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Data-driven approach for bio-medical and healthcare

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-driven approach for bio-medical and healthcare/ edited by Nilanjan Dey.
其他作者:	Dey, Nilanjan.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	xiii, 233 p. :ill., digital ;24 cm.
內容註:	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 A_ected 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.
Contained By:	Springer Nature eBook
標題:	Medicine - Data processing. -
電子資源:	https://doi.org/10.1007/978-981-19-5184-8
ISBN:	9789811951848

Data-driven approach for bio-medical and healthcare
Data-driven approach for bio-medical and healthcare[electronic resource] /edited by Nilanjan Dey. - Singapore :Springer Nature Singapore :2023. - xiii, 233 p. :ill., digital ;24 cm. - Data-intensive research,2731-5568. - Data-intensive research..

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 A_ected 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.

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.

ISBN: 9789811951848

Standard No.: 10.1007/978-981-19-5184-8doiSubjects--Topical Terms:

661309
Medicine
--Data processing.

LC Class. No.: R858

Dewey Class. No.: 610.285

Data-driven approach for bio-medical and healthcare
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520 $a 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.
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