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    임상근거지식 추출을 위한 메디컬 인포매틱스 기법 = Medical informatics methods for the clinical evidence extraction

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    https://www.riss.kr/link?id=A104784222

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Clinical professionals gain new information to assist in patient care when they read the medical literature. Similarly, in clinical preventive medicine, medical science documents that have previously published can be searched and evaluated in order to confirm the scientific support for the clinical preventive medical service offered in order to prevent chronic disease. This paper introduces the medical informatics techniques for knowledge extraction that can become the basis for clinical practice. Particularly, it discusses the clinical document retrieval and knowledge discovery tools that can search for extracting the knowledge which the medical expert desires with data mining techniques. For example, Clinical medical personnel and medical researchers can locate the information from the latest literature rapidly or find and evaluate the scientific basis for the treatment and prevention of infection. This study can be used when they analyze the correlation between accumulated and different type of data and contributes to the detection of new knowledge. Recently, the concern about the visualization of massive data and information is high as the importance of big data has received greater attention. Contributions to this technique and decision support tools will increase gradually due to the way support for decision-making through scientific evidence for the pattern changing disease is evaluated or as one of the clinical practice guidelines is accepted.
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    Clinical professionals gain new information to assist in patient care when they read the medical literature. Similarly, in clinical preventive medicine, medical science documents that have previously published can be searched and evaluated in order to...

    Clinical professionals gain new information to assist in patient care when they read the medical literature. Similarly, in clinical preventive medicine, medical science documents that have previously published can be searched and evaluated in order to confirm the scientific support for the clinical preventive medical service offered in order to prevent chronic disease. This paper introduces the medical informatics techniques for knowledge extraction that can become the basis for clinical practice. Particularly, it discusses the clinical document retrieval and knowledge discovery tools that can search for extracting the knowledge which the medical expert desires with data mining techniques. For example, Clinical medical personnel and medical researchers can locate the information from the latest literature rapidly or find and evaluate the scientific basis for the treatment and prevention of infection. This study can be used when they analyze the correlation between accumulated and different type of data and contributes to the detection of new knowledge. Recently, the concern about the visualization of massive data and information is high as the importance of big data has received greater attention. Contributions to this technique and decision support tools will increase gradually due to the way support for decision-making through scientific evidence for the pattern changing disease is evaluated or as one of the clinical practice guidelines is accepted.

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    참고문헌 (Reference)

    1 Lam W, "Using a Bayesian network induction approach for text categorization" Morgan Kauffman 745-750, 1997

    2 Huan Liu, "Toward integrating feature selection algorithms for classification and clustering" 17 : 491-502, 2005

    3 Vapnik VN, "Statistical learning theory" Wiley-Interscience 1998

    4 Stoyanovich J, "Semantic ranking and result visualization for life sciences publications" IEEE 860-871, 2010

    5 Buchanan BG, "Rule based expert systems: the MYCIN experiments of the Stanford Heuristic Programming Project" Addison-Wesley Longman Publishing 1984

    6 "Pub anatomy: integrated exploration of biomedical literature and data" Microarray Lab

    7 Duda RO, "Pattern classification. 2nd ed" Wiley-Interscience 2000

    8 Nikitin A, "Pathway studio: the analysis and navigation of molecular networks" 19 : 2155-2157, 2003

    9 Domingos P, "On the optimality of the simple Bayesian classifier under zero-one loss" 29 : 103-130, 1997

    10 Pan F, "Multi-dimensional fragment classification in biomedical text" Queen’s University 2006

    1 Lam W, "Using a Bayesian network induction approach for text categorization" Morgan Kauffman 745-750, 1997

    2 Huan Liu, "Toward integrating feature selection algorithms for classification and clustering" 17 : 491-502, 2005

    3 Vapnik VN, "Statistical learning theory" Wiley-Interscience 1998

    4 Stoyanovich J, "Semantic ranking and result visualization for life sciences publications" IEEE 860-871, 2010

    5 Buchanan BG, "Rule based expert systems: the MYCIN experiments of the Stanford Heuristic Programming Project" Addison-Wesley Longman Publishing 1984

    6 "Pub anatomy: integrated exploration of biomedical literature and data" Microarray Lab

    7 Duda RO, "Pattern classification. 2nd ed" Wiley-Interscience 2000

    8 Nikitin A, "Pathway studio: the analysis and navigation of molecular networks" 19 : 2155-2157, 2003

    9 Domingos P, "On the optimality of the simple Bayesian classifier under zero-one loss" 29 : 103-130, 1997

    10 Pan F, "Multi-dimensional fragment classification in biomedical text" Queen’s University 2006

    11 Nawaz R, "Metaknowledge annotation of bio-events" European Language Resources Association 2498-2505, 2010

    12 Grishman R, "Message understanding conference-6: a brief history" Association for ComputationalLinguistics 466-471, 1996

    13 Cho SB, "Machine learning in DNA microarray analysis for cancer classification" Australian Computer Society 189-198, 2003

    14 Orr MJ, "Introduction to radial basis function networks" Centre for Cognitive Science 1996

    15 Tan PN, "Introduction to data mining" Addison Wesley Longman 2007

    16 Anbarasi M, "Enhanced prediction of heart disease with feature subset selection using genetic algorithm" 2 : 5370-5376, 2010

    17 Yu H, "Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS" 11 (11): S6-, 2010

    18 Aronson AR, "Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program" 17-21, 2001

    19 Woolf SH, "Clinical guidelines: potential benefits, limitations, and harms of clinical guidelines" 318 : 527-530, 1999

    20 Heckerman D, "Bayesian networks for data mining" 1 : 79-119, 1997

    21 Silla CN, "Automatic text summarization with genetic algorithm-based attribute selection" 3315 : 305-314, 2004

    22 Kim SN, "Automatic classification of sentences to support evidence based medicine" 12 (12): S5-, 2011

    23 Cristianini N, "An introduction to support vector machines and other kernel-based learning methods" Cambridge University Press 2000

    24 Plake C, "AliBaba: PubMed as a graph" 22 : 2444-2445, 2006

    25 Baharudin B, "A review of machine learning algorithms for text-documents classification" 1 : 4-20, 2010

    26 Yang Y, "A comparative study on feature selection in text categorization" Morgan Kaufmann 412-420, 1997

    27 송미화, "A Multi-Classifier Based Guideline Sentence Classification System" 대한의료정보학회 17 (17): 224-231, 2011

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    학술지 이력

    학술지 이력
    연월일 이력구분 이력상세 등재구분
    2024 평가 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
    2021-01-01 등재 등재학술지 선정 (해외등재 학술지 평가) KCI등재
    2020-12-01 등재 등재 탈락 (해외등재 학술지 평가)
    2013-10-01 등재 등재학술지 선정 (기타) KCI등재
    2011-01-01 등재 등재후보학술지 유지 (기타) KCI등재후보
    2007-01-01 등재 SCOPUS 등재 (신규평가) KCI등재후보
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    학술지 인용정보

    학술지 인용정보
    기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
    2016 0.33 0.33 0.48
    KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
    0.5 0.57 0.815 0.12
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