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Methods Inf Med 2009; 48(03): 225-228
DOI: 10.1055/s-0038-1625129
Editorial
Schattauer GmbH

Biomedical Data Mining

N. Peek
1   Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
,
C. Combi
2   Department of Computer Science, University of Verona, Verona, Italy
,
A. Tucker
3   School of Information Systems, Computing and Mathematics, Brunel, London, UK
› Author Affiliations
Further Information

Publication History

Publication Date:
22 January 2018 (online)

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Summary

Objective: To introduce the special topic of Methods of Information in Medicine on data mining in biomedicine, with selected papers from two workshops on Intelligent Data Analysis in bioMedicine (IDAMAP) held in Verona (2006) and Amsterdam (2007).

Methods: Defining the field of biomedical data mining. Characterizing current developments and challenges for researchers in the field. Reporting on current and future activities of IMIA’s working group on Intelligent Data Analysis and Data Mining. Describing the content of the selected papers in this special topic.

Results and Conclusions: In the biomedical field, data mining methods are used to develop clinical diagnostic and prognostic systems, to interpret biomedical signal and image data, to discover knowledge from biological and clinical databases, and in biosurveillance and anomaly detection applications. The main challenges for the field are i) dealing with very large search spaces in a both computationally efficient and statistically valid manner, ii) incorporating and utilizing medical and biological background knowledge in the data analysis process, iii) reasoning with time-oriented data and temporal abstraction, and iv) developing end-user tools for interactive presentation, interpretation, and analysis of large datasets.

Keywords

Data mining - machine learning
 

  • References

  • 1 Fayyad U, Piatetsky-Shapiro G, Smyth P. The KDD process for extracting useful knowledge from volumes of data. Commun ACM 1996; 39 (11) 27-34.
    PubMedGoogle Scholar
  • 2 Hand DJ, Mannila H, Smyth P. Principles of Data Mining. Cambridge, Massachusetts: MIT Press; 2001
    Google Scholar
  • 3 Giudici P. Applied Data Mining Statistical Methods for Business and Industry. London: John Wiley & Sons; 2003
    Google Scholar
  • 4 Han J, Kamber M. Data Mining. Concepts and Techniques. San Francisco, California: Morgan Kaufmann Publishers; 2006
    Google Scholar
  • 5 Abu-Hanna A, Lucas PJ. Prognostic models in medicine: AI and statistical approaches. Methods Inf Med 2001; 40 (01) 1-5.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 6 Bellazzi R, Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform 2008; 77 (02) 81-97.
    CrossrefPubMedGoogle Scholar
  • 7 Lavrac N, Kononenko I, Keravnou E, Kukar M, Zupan B. Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction. AI Commun 1998; 11: 191-218.
    PubMedGoogle Scholar
  • 8 Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 2001; 23: 89-109.
    CrossrefPubMedGoogle Scholar
  • 9 Sakai S, Kobayashi K, Nakamura J, Toyabe S, Akazawa K. Accuracy in the diagnostic prediction of acute appendicitis based on the Bayesian network model. Methods Inf Med 2007; 46 (06) 723-726.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 10 Pfaff M, Weller K, Woetzel D, Guthke R, Schroeder K, Stein G, Pohlmeier R, Vienken J. Prediction of cardiovascular risk in hemodialysis patients by data mining. Methods Inf Med 2004; 43 (01) 106-113.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 11 Tjortjis C, Saraee M, Theodoulidis B, Keane JA. Using T3, an improved decision tree classifier, for mining stroke-related medical data. Methods Inf Med 2007; 46 (05) 523-529.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 12 Verduijn M, Peek N, de Keizer NF, van Lieshout EJ, de Pont AC, Schultz MJ, de Jonge E, de Mol BA. Individual and joint expert judgments as reference standards in artifact detection. J Am Med Inform Assoc 2008; 15 (02) 227-234.
    CrossrefPubMedGoogle Scholar
  • 13 Jakkula V, Cook DJ. Anomaly detection using temporal data mining in a smart home environment. Methods Inf Med 2008; 47 (01) 70-75.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 14 Nannings B, Bosman RJ, Abu-Hanna A. A subgroup discovery approach for scrutinizing blood glucose management guidelines by the identification of hyperglycemia determinants in ICU patients. Methods Inf Med 2008; 47 (06) 480-488.
    PubMedGoogle Scholar
  • 15 Lessmann B, Nattkemper TW, Hans VH, Degen-hard A. A method for linking computed image features to histological semantics in neuropathology. J Biomed Inform 2007; 40 (06) 631-641.
    CrossrefPubMedGoogle Scholar
  • 16 www.who.int/whosis/icd10. Last accessed Mar 3, 2009
    PubMed
  • 17 Hernán MA. A definition of causal effect for epidemiological research. J Epidemiol Community Health 2004; 58: 265-271.
    CrossrefPubMedGoogle Scholar
  • 18 Barker G, Batley J, O’Sullivan H, Edwards KJ, Edwards D. Redundancy based detection of sequence polymorphisms in expressed sequence tag data using auto SNP. Bioinformatics 2003; 19 (03) 421-422.
    CrossrefPubMedGoogle Scholar
  • 19 Friedman N, Linial M, Nachman I, Pe’er D. Using Bayesian networks to analyze expression data. J Comput Biol 2000; 7 3–4 601-620.
    CrossrefPubMedGoogle Scholar
  • 20 Lobley A, Swindells MB, Orengo CA, Jones DT. Inferring function using patterns of native disorder in proteins. PLoS Comput Biol 2007; 3 (08) e162.
    CrossrefPubMedGoogle Scholar
  • 21 Choi JK, Yu U, Kim S, Yoo OJ. Combining multiple microarray studies and modeling interstudy variation. Bioinformatics 2003; 19 (01) i84-i90.
    CrossrefPubMedGoogle Scholar
  • 22 Jelier R, Schuemie MJ, Roes PJ, van Mulligen EM, Kors JA. Literature-based concept profiles for gene annotation: the issue of weighting. Int J Med Inform 2008; 77 (05) 354-362.
    CrossrefPubMedGoogle Scholar
  • 23 Steele E, Tucker A. Consensus and meta-analysis regulatory networks for combining multiple micro-array gene expression datasets. J Biomed Inform 2008; 41 (06) 914-926.
    CrossrefPubMedGoogle Scholar
  • 24 Husmeier D, Werhli AV. Bayesian integration of biological prior knowledge into the reconstruction of gene regulatory networks with Bayesian networks. In: Markstein P, Xu Y. editors. Computational Systems Bioinformatics, Volume 6: Proceedings of the CSB 2007 Conference. World Scientific; 2007. pp 85-95.
    Google Scholar
  • 25 Kitano H. Computational systems biology. Nature 2002; 420: 206-210.
    CrossrefPubMedGoogle Scholar
  • 26 Zhang M. Interactive analysis of systems biology molecular expression data. BMC Systems Biol 2008; 2: 2-23.
    CrossrefPubMedGoogle Scholar
  • 27 Witten IH, Frank E. Data Mining. Practical Machine Learning Tools and Techniques. San Franciso, California: Morgan Kaufmann Publishers; 2005
    Google Scholar
  • 28 http://www.ailab.si/orange. Last accessed Mar 3, 2009
    PubMed
  • 29 Zupan B, Holmes JH, Bellazzi R. Knowledge-based data analysis and interpretation. Artif Intell Med 2006; 37 (03) 163-165.
    CrossrefPubMedGoogle Scholar
  • 30 Shahar Y. Dimensions of time in illness: an objective view. Ann Intern Med 2000; 132 (01) 45-53.
    CrossrefPubMedGoogle Scholar
  • 31 Combi C, Shahar Y. Temporal reasoning and temporal data maintenance in medicine: issues and challenges. Comput Biol Med 1997; 27 (05) 353-368.
    CrossrefPubMedGoogle Scholar
  • 32 Adlassnig KP, Combi C, Das AK, Keravnou ET, Pozzi G. Temporal representation and reasoning in medicine: Research directions and challenges. Artif Intell Med 2006; 38 (02) 101-113.
    CrossrefPubMedGoogle Scholar
  • 33 Stacey M, McGregor C. Temporal abstraction in intelligent clinical data analysis: a survey. Artif Intell Med 2007; 39 (01) 1-24.
    CrossrefPubMedGoogle Scholar
  • 34 http://magix.fri.uni-lj.si/idadm. Last accessed Mar 3, 2009
    PubMed
  • 35 http://www.idamap.org. Last accessed Mar 3, 2009
    PubMed
  • 36 http://www.amia.org/mbrcenter/wg/kddm. Last accessed Mar 3, 2009
    PubMed
  • 37 Curk T, Petrovic U, Shaulsky G, Zupan B. Rule-based clustering for gene promoter structure discovery. Methods Inf Med 2009; 48: 229-235.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 38 Bielza C, Robles V, Larrañaga P. Estimation of distribution algorithms as logistic regression regularizers of microarray classifiers. Methods Inf Med 2009; 48: 236-241.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 39 Andreassen S, Zalounina A, Leibovici L, Paul M. Learning susceptibility of a pathogen to antibiotics using data from similar pathogens. Methods Inf Med 2009; 48: 242-247.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 40 Castellani U, Cristani M, Daducci A, Farace P, Marzola P, Murino V, Sbarbati V. DCE-MRI data analysis for cancer area classification. Methods Inf Med 2009; 48: 248-253.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 41 Klimov D, Shahar Y, Taieb-Maimon M. Intelligent interactive visual exploration of temporal associations among multiple time-oriented patient records. Methods Inf Med 2009; 48: 254-262.
    Article in Thieme ConnectPubMedGoogle Scholar