000 | 03972nam a22005895i 4500 | ||
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001 | 978-981-19-5184-8 | ||
003 | DE-He213 | ||
005 | 20240207153455.0 | ||
007 | cr nn 008mamaa | ||
008 | 221027s2023 si | s |||| 0|eng d | ||
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_a9789811951848 _9978-981-19-5184-8 |
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_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. |
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300 |
_aXIII, 233 p. 102 illus., 81 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aData-Intensive Research, _x2731-5568 |
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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. | ||
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_fUABC ; _cPerpetuidad |
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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 |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789811951831 |
776 | 0 | 8 |
_iPrinted edition: _z9789811951855 |
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_iPrinted edition: _z9789811951862 |
830 | 0 |
_aData-Intensive Research, _x2731-5568 |
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856 | 4 | 0 |
_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-19-5184-8 |
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912 | _aZDB-2-SXIT | ||
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