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Artificial Intelligence in Medicine

Print ISSN
0933-3657
Electronic ISSN
1873-2860
Impact factor
1.568
Publisher
Sciencedirect
URL
http://www.sciencedirect.com/science/journal/09333657
Usage rank
7589
Article count
936
Free count
8
Free percentage
0.00854701
PDFs via platforms
Sciencedirect, Gale, Ingenta, Proquest, Ebscoatoz, Ebsconet, Rcgp, and CSA

  1. Editorial Board
    Author(s) unavailable

    Artificial Intelligence in Medicine 63(1):IFC (2015)

  2. Improving the Mann–Whitney statistical test for feature selection: An approach in breast cancer diagnosis on mammography

    Artificial Intelligence in Medicine 63(1):19 (2015) PMID 25555756

    • An innovative feature selection method (named uFilter) is proposed. • A set of image-based features, from mammography lesions, were explored and successfully ran...
  3. Editorial Board
    Author(s) unavailable

    Artificial Intelligence in Medicine 62(3):IFC (2014)

  4. NICeSim: An open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making

    Artificial Intelligence in Medicine 62(3):193 (2014) PMID 25457563

    Highlights • NICeSim is flexible: It allows inclusion/deletion of any variable of interest. • NICeSim is dynamic: It creates a just-in-time ML model from...
  5. Editorial Board
    Author(s) unavailable

    Artificial Intelligence in Medicine 62(2):IFC (2014)

  6. Elie Sanchez, 1944–2014

    Artificial Intelligence in Medicine 62(2):73 (2014)

  7. Editorial Board
    Author(s) unavailable

    Artificial Intelligence in Medicine 61(3):IFC (2014)

  8. Editorial Board
    Author(s) unavailable

    Artificial Intelligence in Medicine 61(2):IFC (2014)

  9. Editorial Board
    Author(s) unavailable

    Artificial Intelligence in Medicine 61(1):CO2 (2014)

  10. Multi-test decision tree and its application to microarray data classification

    Artificial Intelligence in Medicine 61(1):35 (2014) PMID 24630712

    Objective The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human de...