000 03520nam a22005535i 4500
001 978-3-031-44877-5
003 DE-He213
005 20251030110840.0
007 cr nn 008mamaa
008 231126s2024 sz | s |||| 0|eng d
020 _a9783031448775
_9978-3-031-44877-5
024 7 _a10.1007/978-3-031-44877-5
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
_2thema
100 1 _aPrzystalski, Karol.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_967573
245 1 0 _aExplainable Machine Learning in Medicine
_h[electronic resource] /
_cby Karol Przystalski, Rohit M. Thanki.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXI, 82 p. 14 illus., 12 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 _aSynthesis Lectures on Engineering, Science, and Technology,
_x2690-0327
505 0 _a1 Introduction -- 2 Medical Tabular Data -- 3 Natural Language Processing for Medical Data Analysis -- 4 Computer Vision for Medical Data Analysis -- 5 Time Series Data Used for Diseases Recognition and Anomaly Detection -- 6 Summary.
520 _aThis book covers a variety of advanced communications technologies that can be used to analyze medical data and can be used to diagnose diseases in clinic centers. The book is a primer of methods for medicine, providing an overview of explainable artificial intelligence (AI) techniques that can be applied in different medical challenges. The authors discuss how to select and apply the proper technology depending on the provided data and the analysis desired. Because a variety of data can be used in the medical field, the book explains how to deal with challenges connected with each type. A number of scenarios are introduced that can happen in real-time environments, with each pared with a type of machine learning that can be used to solve it. Provides a detailed primer on explainable artificial intelligence (AI) and machine learning (ML) methods that can be used in medical cases Presents how explainable AI can aid in choosing ML algorithms that give better performance in medical applications Covers various kinds of medical data used to diagnose diseases in clinic centers and what technologies are best suited to analyze it.
650 0 _aTelecommunication.
650 0 _aMedical informatics.
_960693
650 0 _aArtificial intelligence.
_958441
650 0 _aBiomedical engineering.
650 1 4 _aCommunications Engineering, Networks.
650 2 4 _aHealth Informatics.
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aBiomedical Engineering and Bioengineering.
700 1 _aThanki, Rohit M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_967574
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031448768
776 0 8 _iPrinted edition:
_z9783031448782
776 0 8 _iPrinted edition:
_z9783031448799
830 0 _aSynthesis Lectures on Engineering, Science, and Technology,
_x2690-0327
856 4 0 _uhttps://doi.org/10.1007/978-3-031-44877-5
912 _aZDB-2-SXSC
999 _c56644
_d56644