LEADER 04222nam 22006615 450 001 9910842027903321 005 20250911193409.0 010 $a9783031393556 010 $a3031393554 024 7 $a10.1007/978-3-031-39355-6 035 $a(CKB)5670000000814904 035 $a(DE-He213)978-3-031-39355-6 035 $a(MiAaPQ)EBC31727438 035 $a(Au-PeEL)EBL31727438 035 $a(DNLM)9918987868506676 035 $a(EXLCZ)995670000000814904 100 $a20240304d2024 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence and Machine Learning in Health Care and Medical Sciences $eBest Practices and Pitfalls /$fedited by Gyorgy J. Simon, Constantin Aliferis 205 $a1st ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (XXVI, 810 p. 146 illus., 130 illus. in color.) 225 1 $aHealth Informatics,$x2197-3741 311 08$a9783031393549 311 08$a3031393546 327 $aPredictive Analytics -- Machine Learning -- Artificial Intelligence -- Data Mining -- Clinical Risk Models -- Clinical Risk Stratification -- Data Science -- Causal Discovery -- Causal Inference -- Causal Discovery in Health Sciences -- Causal Inference In Health Sciences -- Ehr Data Analytics -- Medical Knowledge Discovery -- Biomedical Machine Learning -- Biomedical Artificial Intelligence -- Healthcare Machine Learning -- Healthcare Artificial Intelligence -- Translational Science Machine Learning -- Machine Learning for Biological Discovery -- Machine Learning in Bioinformatics -- Machine Learning in Genomics. 330 $aThis open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls is a comprehensive guide to how AI and ML techniques can best be applied in health care. The emphasis placed on how to avoid a variety of pitfalls that can be encountered makes it an indispensable guide for all medical informatics professionals and physicians who utilize these methodologies on a day-to-day basis. Furthermore, this work will be of significant interest to health data scientists, administrators and to students in the health sciences seeking an up-to-date resource on the topic. 410 0$aHealth Informatics,$x2197-3741 606 $aMedical informatics 606 $aMedical care 606 $aBioinformatics 606 $aPublic health 606 $aHealth Informatics 606 $aHealth Care 606 $aBioinformatics 606 $aPublic Health 615 0$aMedical informatics. 615 0$aMedical care. 615 0$aBioinformatics. 615 0$aPublic health. 615 14$aHealth Informatics. 615 24$aHealth Care. 615 24$aBioinformatics. 615 24$aPublic Health. 676 $a610.285 702 $aSimon$b Gyorgy J$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAliferis$b Constantin$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910842027903321 996 $aArtificial Intelligence and Machine Learning in Health Care and Medical Sciences$94145731 997 $aUNINA