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_223
245 1 0 _aData-Driven Approach for Bio-medical and Healthcare
_h[electronic resource] /
_cedited by Nilanjan Dey.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXIII, 233 p. 102 illus., 81 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aData-Intensive Research,
_x2731-5568
500 _aAcceso multiusuario
505 0 _aChapter 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.
520 _aThe 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.
541 _fUABC ;
_cPerpetuidad
650 0 _aComputational intelligence.
650 0 _aQuantitative research.
650 0 _aArtificial intelligence.
650 0 _aInternet of things.
650 1 4 _aComputational Intelligence.
650 2 4 _aData Analysis and Big Data.
650 2 4 _aArtificial Intelligence.
650 2 4 _aInternet of Things.
700 1 _aDey, Nilanjan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811951831
776 0 8 _iPrinted edition:
_z9789811951855
776 0 8 _iPrinted edition:
_z9789811951862
830 0 _aData-Intensive Research,
_x2731-5568
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-19-5184-8
912 _aZDB-2-INR
912 _aZDB-2-SXIT
942 _cLIBRO_ELEC
999 _c260545
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