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001452309 001__ 1452309
001452309 003__ OCoLC
001452309 005__ 20230310003349.0
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001452309 019__ $$a1356795866$$a1356797975$$a1357016578
001452309 020__ $$a9783031111990$$q(electronic bk.)
001452309 020__ $$a3031111990$$q(electronic bk.)
001452309 020__ $$z3031111982
001452309 020__ $$z9783031111983
001452309 0247_ $$a10.1007/978-3-031-11199-0$$2doi
001452309 035__ $$aSP(OCoLC)1363828181
001452309 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCQ$$dYDX$$dUKAHL
001452309 049__ $$aISEA
001452309 050_4 $$aR859.7.A78$$bT74 2022
001452309 08204 $$a610.285$$223/eng/20230123
001452309 24500 $$aTrends of artificial intelligence and big data for e-health /$$cHouneida Sakly, Kristen Yeom, Safwan Halabi, Mourad Said, Jayne Seekins, Moncef Tagina, editors.
001452309 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2022]
001452309 300__ $$a1 online resource (1 volume) :$$billustrations (black and white, and colour)
001452309 336__ $$atext$$btxt$$2rdacontent
001452309 337__ $$acomputer$$bc$$2rdamedia
001452309 338__ $$aonline resource$$bcr$$2rdacarrier
001452309 4901_ $$aIntegrated science ;$$vvolume 9
001452309 504__ $$aIncludes bibliographical references.
001452309 5050_ $$a1. AI and Big Data for Intelligent Health: Promise and Potential -- 2. AI and Big Data for Cancer Segmentation, Detection and Prevention -- 3. Radiology, AI and Big Data: Challenges and Opportunities for Medical Imaging -- 4. Neuroradiology: Current Status and Future Prospects -- 5. Big Data and AI in Cardiac Imaging -- 6. Artificial Intelligence and Big data for COVID-19 Diagnosis -- 7. AI and Big Data for Drug Discovery -- 8. Blockchain of IoMT (BIoMT): A New Paradigm for COVID-19 Pandemic: Application, Architecture, Technology, and Security -- 9. AI and Big Data for Therapeutic Strategies in Psychiatry -- 10. Distributed Learning in Healthcare -- 11. Cybersecurity in Healthcare -- 12. Radiology and Radiomics: Towards oncology Prediction with IA and Big Data.
001452309 506__ $$aAccess limited to authorized users.
001452309 520__ $$aThis book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others. This will include topics such as Artificial Intelligence of Thing (AIOT), Explainable Artificial Intelligence (XAI), Distributed learning, Blockchain of Internet of Things (BIOT), Cybersecurity, and Internet of (Medical) Things (IoTs). Healthcare providers will learn how to leverage Big Data analytics and AI as methodology for accurate analysis based on their clinical data repositories and clinical decision support. The capacity to recognize patterns and transform large amounts of data into usable information for precision medicine assists healthcare professionals in achieving these objectives. Intelligent Health has the potential to monitor patients at risk with underlying conditions and track their progress during therapy. Some of the greatest challenges in using these technologies are based on legal and ethical concerns of using medical data and adequately representing and servicing disparate patient populations. One major potential benefit of this technology is to make health systems more sustainable and standardized. Privacy and data security, establishing protocols, appropriate governance, and improving technologies will be among the crucial priorities for Digital Transformation in Healthcare.
001452309 588__ $$aDescription based on print version record.
001452309 650_0 $$aArtificial intelligence$$xMedical applications.
001452309 650_0 $$aMedical informatics.
001452309 650_0 $$aBig data.
001452309 655_0 $$aElectronic books.
001452309 7001_ $$aSakly, Houneida.$$eeditor.
001452309 7001_ $$aYeom, Kristen.$$eeditor.
001452309 7001_ $$aHalabi, Safwan.$$eeditor.
001452309 7001_ $$aSaid, Mourad.$$eeditor.
001452309 7001_ $$aSeekins, Jayne.$$eeditor.
001452309 7001_ $$aTagina, Moncef.$$eeditor.
001452309 77608 $$iPrint version:$$tTRENDS OF ARTIFICIAL INTELLIGENCE AND BIG DATA FOR E-HEALTH.$$d[Place of publication not identified] : SPRINGER INTERNATIONAL PU, 2022$$z3031111982$$w(OCoLC)1330403759
001452309 830_0 $$aIntegrated science ;$$vv. 9.
001452309 852__ $$bebk
001452309 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-11199-0$$zOnline Access$$91397441.1
001452309 909CO $$ooai:library.usi.edu:1452309$$pGLOBAL_SET
001452309 980__ $$aBIB
001452309 980__ $$aEBOOK
001452309 982__ $$aEbook
001452309 983__ $$aOnline
001452309 994__ $$a92$$bISE