LEADER 05230 am 22007813u 450 001 9910293143703321 005 20230125211840.0 010 $a3-319-78503-6 024 7 $a10.1007/978-3-319-78503-5 035 $a(CKB)4100000004243864 035 $a(DE-He213)978-3-319-78503-5 035 $a(MiAaPQ)EBC5394754 035 $a(Au-PeEL)EBL5394754 035 $a(OCoLC)1078960705 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/57605 035 $a(PPN)227406605 035 $a(EXLCZ)994100000004243864 100 $a20180514d2018 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aClinical Text Mining$b[electronic resource] $eSecondary Use of Electronic Patient Records /$fby Hercules Dalianis 205 $a1st ed. 2018. 210 $cSpringer Nature$d2018 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XVII, 181 p. 54 illus., 28 illus. in color.) 311 $a3-319-78502-8 327 $aIntroduction -- The history of the patient record and the paper record -- User needs: clinicians, clinical researchers and hospital management -- Characteristics of patient records and clinical corpora -- Medical classifications and terminologies -- Evaluation metrics and evaluation -- Basic building blocks for clinical text processing -- Computational methods for text analysis and text classification -- Ethics and privacy of patient records for clinical text mining research -- Applications of clinical text mining -- Networks and shared tasks in clinical text mining -- Conclusions and outlook -- References -- Index. 330 $aThis open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book?s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields. 606 $aInformation storage and retrieval 606 $aHealth informatics 606 $aNatural language processing (Computer science) 606 $aData mining 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/H28009 606 $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23060 606 $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 610 $aNatural Language Processing 610 $aText Analysis 610 $aData Mining 610 $aHealth Informatics 610 $aText Mining 610 $aMedical Terminologies 610 $aHealth Care Information Systems 610 $aSupport Vector Machines 615 0$aInformation storage and retrieval. 615 0$aHealth informatics. 615 0$aNatural language processing (Computer science). 615 0$aData mining. 615 14$aInformation Storage and Retrieval. 615 24$aHealth Informatics. 615 24$aNatural Language Processing (NLP). 615 24$aHealth Informatics. 615 24$aNatural Language Processing (NLP). 615 24$aData Mining and Knowledge Discovery. 676 $a025.04 700 $aDalianis$b Hercules$4aut$4http://id.loc.gov/vocabulary/relators/aut$0994610 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910293143703321 996 $aClinical Text Mining$92277695 997 $aUNINA