Machine Learning for Health Informatics

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9605)

Abstract

Machine Learning (ML) studies algorithms which can learn from data to gain knowledge from experience and to make decisions and predictions. Health Informatics (HI) studies the effective use of probabilistic information for decision making. The combination of both has greatest potential to rise quality, efficacy and efficiency of treatment and care. Health systems worldwide are confronted with “big data” in high dimensions, where the inclusion of a human is impossible and automatic ML (aML) show impressive results. However, sometimes we are confronted with complex data, “little data”, or rare events, where aML-approaches suffer of insufficient training samples. Here interactive ML (iML) may be of help, particularly with a doctor-in-the-loop, e.g. in subspace clustering, k-Anonymization, protein folding and protein design. However, successful application of ML for HI needs an integrated approach, fostering a concerted effort of four areas: (1) data science, (2) algorithms (with focus on networks and topology (structure), and entropy (time), (3) data visualization, and last but not least (4) privacy, data protection, safety & security.

Keywords

Machine learning Health informatics 

References

  1. 1.
    Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3, 210–229 (1959)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10, 988–999 (1999)CrossRefGoogle Scholar
  3. 3.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory COLT, pp. 144–152. ACM (1992)Google Scholar
  4. 4.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2009)CrossRefMATHGoogle Scholar
  5. 5.
    Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015)MathSciNetCrossRefGoogle Scholar
  6. 6.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar
  7. 7.
    Holzinger, A., Dehmer, M., Jurisica, I.: Knowledge discovery and interactive data mining in bioinformatics - state-of-the-art, future challenges and research directions. BMC Bioinform. 15, I1 (2014)CrossRefGoogle Scholar
  8. 8.
    Holzinger, A.: Trends in interactive knowledge discovery for personalized medicine: cognitive science meets machine learning. IEEE Intell. Inform. Bull. 15, 6–14 (2014)Google Scholar
  9. 9.
    Su, X., Kang, J., Fan, J., Levine, R.A., Yan, X.: Facilitating score and causal inference trees for large observational studies. J. Mach. Learn. Res. 13, 2955–2994 (2012)MathSciNetMATHGoogle Scholar
  10. 10.
    Huppertz, B., Holzinger, A.: Biobanks a source of large biological data sets: open problems and future challenges. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 317–330. Springer, Heidelberg (2014)Google Scholar
  11. 11.
    Mattmann, C.A.: Computing: a vision for data science. Nature 493, 473–475 (2013)CrossRefGoogle Scholar
  12. 12.
    Otasek, D., Pastrello, C., Holzinger, A., Jurisica, I.: Visual data mining: effective exploration of the biological universe. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 19–33. Springer, Heidelberg (2014)Google Scholar
  13. 13.
    Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature 521, 452–459 (2015)CrossRefGoogle Scholar
  14. 14.
    Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical Dirichlet processes. J. Am. Stat. Assoc. 101, 1566–1581 (2006)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Houlsby, N., Huszar, F., Ghahramani, Z., Hernndez-lobato, J.M.: Collaborative gaussian processes for preference learning. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advances in Neural Information Processing Systems (NIPS 2012), pp. 2096–2104 (2012)Google Scholar
  16. 16.
    Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the human out of the loop: a review of bayesian optimization. Proc. IEEE 104, 148–175 (2016)CrossRefGoogle Scholar
  17. 17.
    Clark, A.: Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 181–204 (2013)CrossRefGoogle Scholar
  18. 18.
    Lee, S., Holzinger, A.: Knowledge discovery from complex high dimensional data. In: Michaelis, S., Piatkowski, N., Stolpe, M. (eds.) Solving Large Scale Learning Tasks. Challenges and Algorithms. LNCS (LNAI), vol. 9580, pp. 148–167. Springer, Heidelberg (2016). doi:10.1007/978-3-319-41706-6_7 CrossRefGoogle Scholar
  19. 19.
    Mayer, C., Bachler, M., Holzinger, A., Stein, P., Wassertheurer, S.: The effect of threshold values and weighting factors on the association between entropy measures and mortality after myocardial infarction in the cardiac arrhythmia suppression trial. Entropy 18, 1–15 (2016)CrossRefGoogle Scholar
  20. 20.
    Jadad, A.R., OGrady, L.: How should health be defined? Br. Med. J. 337, a2900 (2008)CrossRefGoogle Scholar
  21. 21.
    Parry, D.: Health informatics. In: Kasabov, N. (ed.) Springer Handbook of Bio-/Neuro-informatics, pp. 555–564. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  22. 22.
    Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inform. (BRIN) 3, 119–131 (2016)CrossRefGoogle Scholar
  23. 23.
    Holzinger, A., Plass, M., Holzinger, K., Crişan, G.C., Pintea, C.-M., Palade, V.: Towards interactive Machine Learning (iML): applying ant colony algorithms to solve the traveling salesman problem with the human-in-the-loop approach. In: Buccafurri, F., Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-ARES 2016. LNCS, vol. 9817, pp. 81–95. Springer, Heidelberg (2016). doi:10.1007/978-3-319-45507-5_6 CrossRefGoogle Scholar
  24. 24.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37–54 (1996)Google Scholar
  25. 25.
    Holzinger, A.: On topological data mining. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 331–356. Springer, Heidelberg (2014)Google Scholar
  26. 26.
    Ward, M., Grinstein, G., Keim, D.: Interactive Data Visualization: Foundations, Techniques, and Applications. AK Peters Ltd., Natick (2010)MATHGoogle Scholar
  27. 27.
    Holzinger, A.: On knowledge discovery and interactive intelligent visualization of biomedical data - challenges in human computer interaction & biomedical informatics. In: Helfert, M., Fancalanci, C., Filipe, J. (eds.) DATA 2012, International Conference on Data Technologies and Applications, pp. 5–16 (2012)Google Scholar
  28. 28.
    Holzinger, A.: Human-computer interaction and knowledge discovery (HCI-KDD): what is the benefit of bringing those two fields to work together? In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 319–328. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40511-2_22 CrossRefGoogle Scholar
  29. 29.
    Holzinger, A., Jurisica, I.: Knowledge discovery and data mining in biomedical informatics: the future is in integrative, interactive machine learning solutions. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 1–18. Springer, Heidelberg (2014)Google Scholar
  30. 30.
    Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)CrossRefGoogle Scholar
  31. 31.
    Tenenbaum, J.B., Kemp, C., Griffiths, T.L., Goodman, N.D.: How to grow a mind: statistics, structure, and abstraction. Science 331, 1279–1285 (2011)MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Burges, C.J.: Dimension reduction: a guided tour. Found. Trends Mach. Learn. 2, 275–365 (2010)CrossRefMATHGoogle Scholar
  33. 33.
    Kandel, E.R., Schwartz, J.H., Jessell, T.M., Siegelbaum, S.A., Hudspeth, A.: Principles of Neural Science, 5th edn. McGraw-Hill, New York (2012). (1760 pages)Google Scholar
  34. 34.
    McDermott, J.E., Wang, J., Mitchell, H., Webb-Robertson, B.J., Hafen, R., Ramey, J., Rodland, K.D.: Challenges in biomarker discovery: combining expert insights with statistical analysis of complex omics data. Expert Opin. Med. Diagn. 7, 37–51 (2013)CrossRefGoogle Scholar
  35. 35.
    Swan, A.L., Mobasheri, A., Allaway, D., Liddell, S., Bacardit, J.: Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology. Omics- J. Integr. Biol. 17, 595–610 (2013)CrossRefGoogle Scholar
  36. 36.
    Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Report, May 2011 (available online)Google Scholar
  37. 37.
    Goolsby, A.W., Olsen, L., McGinnis, M., Grossmann, C.: Clincial data as the basic staple of health learning - creating and protecting a public good. National Institute of Health (2010)Google Scholar
  38. 38.
    Holzinger, A.: Lecture 2 fundamentals of data, information, and knowledge. In: Biomedical Informatics: Discovering Knowledge in Big Data, pp. 57–107. Springer, Cham (2014)Google Scholar
  39. 39.
    Jeanquartier, F., Jean-Quartier, C., Schreck, T., Cemernek, D., Holzinger, A.: Integrating open data on cancer in support to tumor growth analysis. In: Renda, M.E., Bursa, M., Holzinger, A., Khuri, S. (eds.) ITBAM 2016. LNCS, vol. 9832, pp. 49–66. Springer, Heidelberg (2016). doi:10.1007/978-3-319-43949-5_4 CrossRefGoogle Scholar
  40. 40.
    Bleiholder, J., Naumann, F.: Data fusion. ACM Comput. Surv. (CSUR) 41, 1–41 (2008)CrossRefGoogle Scholar
  41. 41.
    Lafon, S., Keller, Y., Coifman, R.R.: Data fusion and multicue data matching by diffusion maps. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1784–1797 (2006)CrossRefGoogle Scholar
  42. 42.
    Blanchet, L., Smolinska, A.: Data fusion in metabolomics and proteomics for biomarker discovery. In: Jung, K. (ed.) Statistical Analysis in Proteomics, pp. 209–223. Springer, New York (2016)CrossRefGoogle Scholar
  43. 43.
    Bellegarda, J.R., Monz, C.: State of the art in statistical methods for language and speech processing. Comput. Speech Lang. 35, 163–184 (2016)CrossRefGoogle Scholar
  44. 44.
    Ricci, F., Rokach, L., Shapira, B.: Recommender systems: introduction and challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 1–34. Springer, New York (2015)CrossRefGoogle Scholar
  45. 45.
    Spinrad, N.: Google car takes the test. Nature 514, 528 (2014)CrossRefGoogle Scholar
  46. 46.
    Wilson, A.G., Dann, C., Lucas, C.G., Xing, E.P.: The human kernel. arXiv preprint arXiv:1510.07389 (2015)
  47. 47.
    Settles, B.: From theories to queries: active learning in practice. In: Guyon, I., Cawley, G., Dror, G., Lemaire, V., Statnikov, A. (eds.) Active Learning and Experimental Design Workshop 2010, vol. 16, pp. 1–18. JMLR Proceedings, Sardinia (2011)Google Scholar
  48. 48.
    Hund, M., Sturm, W., Schreck, T., Ullrich, T., Keim, D., Majnaric, L., Holzinger, A.: Analysis of patient groups and immunization results based on subspace clustering. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds.) BIH 2015. LNCS (LNAI), vol. 9250, pp. 358–368. Springer, Heidelberg (2015). doi:10.1007/978-3-319-23344-4_35 CrossRefGoogle Scholar
  49. 49.
    Lathrop, R.H.: The protein threading problem with sequence amino-acid interaction preferences is np-complete. Protein Eng. 7, 1059–1068 (1994)CrossRefGoogle Scholar
  50. 50.
    Aggarwal, C.C.: On k-anonymity and the curse of dimensionality. In: Proceedings of the 31st International Conference on Very Large Data Bases VLDB, pp. 901–909 (2005)Google Scholar
  51. 51.
    Gigerenzer, G., Gaissmaier, W.: Heuristic decision making. Annu. Rev. Psychol. 62, 451–482 (2011)CrossRefGoogle Scholar
  52. 52.
    Harary, F.: Structural Models. An Introduction to the Theory of Directed Graphs. Wiley, New York (1965)MATHGoogle Scholar
  53. 53.
    Strogatz, S.: Exploring complex networks. Nature 410, 268–276 (2001)CrossRefGoogle Scholar
  54. 54.
    Dorogovtsev, S., Mendes, J.: Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford University Press, New York (2003)CrossRefMATHGoogle Scholar
  55. 55.
    Dehmer, M., Emmert-Streib, F., Pickl, S., Holzinger, A. (eds.): Big Data of Complex Networks. CRC Press Taylor & Francis Group, Boca Raton, London, New York (2016)Google Scholar
  56. 56.
    Holzinger, A., Ofner, B., Dehmer, M.: Multi-touch graph-based interaction for knowledge discovery on mobile devices: state-of-the-art and future challenges. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 241–254. Springer, Heidelberg (2014)Google Scholar
  57. 57.
    Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetCrossRefMATHGoogle Scholar
  58. 58.
    Kleinberg, J.: Navigation in a small world. Nature 406, 845 (2000)CrossRefGoogle Scholar
  59. 59.
    Koontz, W., Narendra, P., Fukunaga, K.: A graph-theoretic approach to nonparametric cluster analysis. IEEE Trans. Comput. 100, 936–944 (1976)MathSciNetCrossRefMATHGoogle Scholar
  60. 60.
    Wittkop, T., Emig, D., Truss, A., Albrecht, M., Boecker, S., Baumbach, J.: Comprehensive cluster analysis with transitivity clustering. Nat. Protoc. 6, 285–295 (2011)CrossRefGoogle Scholar
  61. 61.
    Holzinger, A., Malle, B., Bloice, M., Wiltgen, M., Ferri, M., Stanganelli, I., Hofmann-Wellenhof, R.: On the generation of point cloud data sets: the first step in the knowledge discovery process. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 57–80. Springer, Heidelberg (2014)Google Scholar
  62. 62.
    Canutescu, A.A., Shelenkov, A.A., Dunbrack, R.L.: A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci. 12, 2001–2014 (2003)CrossRefGoogle Scholar
  63. 63.
    Jiang, C., Coenen, F., Sanderson, R., Zito, M.: Text classification using graph mining-based feature extraction. Knowl. Based Syst. 23, 302–308 (2010)CrossRefGoogle Scholar
  64. 64.
    Washio, T., Motoda, H.: State of the art of graph-based data mining. ACM SIGKDD Explor. Newsl. 5, 59 (2003)CrossRefGoogle Scholar
  65. 65.
    Cook, D.J., Holder, L.B.: Substructure discovery using minimum description length and background knowledge. J. Artif. Int. Res. 1, 231–255 (1994)Google Scholar
  66. 66.
    Yoshida, K., Motoda, H., Indurkhya, N.: Graph-based induction as a unified learning framework. Appl. Intell. 4, 297–316 (1994)CrossRefGoogle Scholar
  67. 67.
    Dehaspe, L., Toivonen, H.: Discovery of frequent DATALOG patterns. Data Min. Knowl. Discov. 3, 7–36 (1999)CrossRefGoogle Scholar
  68. 68.
    Windridge, D., Bober, M.: A kernel-based framework for medical big-data analytics. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 196–207. Springer, Heidelberg (2014)Google Scholar
  69. 69.
    Zhou, X., Han, H., Chankai, I., Prestrud, A., Brooks, A.: Approaches to text mining for clinical medical records. In: Proceedings of the 2006 ACM Symposium on Applied Computing - SAC 2006, New York, USA, p. 235. ACM, New York (2006)Google Scholar
  70. 70.
    Corley, C.D., Cook, D.J., Mikler, A.R., Singh, K.P.: Text and structural data mining of influenza mentions in Web and social media. Int. J. Environ. Res. Public Health 7, 596–615 (2010)CrossRefGoogle Scholar
  71. 71.
    Chen, H., Sharp, B.M.: Content-rich biological network constructed by mining PubMed abstracts. BMC Bioinform. 5, 147 (2004)CrossRefGoogle Scholar
  72. 72.
    Barabási, A., Gulbahce, N., Loscalzo, J.: Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011)CrossRefGoogle Scholar
  73. 73.
    Cannon, J.W.: The recognition problem: what is a topological manifold? Bull. Am. Math. Soc. 84, 832–866 (1978)MathSciNetCrossRefMATHGoogle Scholar
  74. 74.
    Zomorodian, A.: Computational topology. In: Atallah, M., Blanton, M. (eds.) Algorithms and Theory of Computation Handbook. Applied Algorithms and Data Structures Series, vol. 2, 2nd edn, pp. 1–31. Chapman and Hall/CRC, Boca Raton (2010). doi:10.1201/9781584888215-c3
  75. 75.
    Epstein, C., Carlsson, G., Edelsbrunner, H.: Topological data analysis. Inverse Prob. 27, 120–201 (2011)Google Scholar
  76. 76.
    Edelsbrunner, H., Mucke, E.P.: 3-dimensional alpha-shapes. ACM Trans. Graph. 13, 43–72 (1994)CrossRefMATHGoogle Scholar
  77. 77.
    Wagner, H., Dlotko, P.: Towards topological analysis of high-dimensional feature spaces. Comput. Vis. Image Underst. 121, 21–26 (2014)CrossRefGoogle Scholar
  78. 78.
    Kobayashi, M., Aono, M.: Vector space models for search and cluster mining. In: Berry, M.W. (ed.) Survey of Text Mining: Clustering, Classification, and Retrieval, pp. 103–122. Springer, New York (2004)CrossRefGoogle Scholar
  79. 79.
    Holzinger, A., Schantl, J., Schroettner, M., Seifert, C., Verspoor, K.: Biomedical text mining: open problems and future challenges. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 271–300. Springer, Heidelberg (2014)Google Scholar
  80. 80.
    Wagner, H., Dłotko, P., Mrozek, M.: Computational topology in text mining. In: Ferri, M., Frosini, P., Landi, C., Cerri, A., Fabio, B. (eds.) CTIC 2012. LNCS, vol. 7309, pp. 68–78. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30238-1_8 CrossRefGoogle Scholar
  81. 81.
    Carlsson, G.: Topology and data. Bull. Am. Math. Soc. 46, 255–308 (2009)MathSciNetCrossRefMATHGoogle Scholar
  82. 82.
    Zhu, X.: Persistent homology: an introduction and a new text representation for natural language processing. In: Rossi, F. (ed.) IJCAI. IJCAI/AAAI (2013)Google Scholar
  83. 83.
    Cerri, A., Fabio, B.D., Ferri, M., Frosini, P., Landi, C.: Betti numbers in multidimensional persistent homology are stable functions. Math. Methods Appl. Sci. 36, 1543–1557 (2013)MathSciNetCrossRefMATHGoogle Scholar
  84. 84.
    Bubenik, P., Kim, P.T.: A statistical approach to persistent homology. Homology Homotopy Appl. 9, 337–362 (2007)MathSciNetCrossRefMATHGoogle Scholar
  85. 85.
    Mowshowitz, A.: Entropy and the complexity of graphs: I. An index of the relative complexity of a graph. Bull. Math. Biophys. 30, 175–204 (1968)MathSciNetCrossRefMATHGoogle Scholar
  86. 86.
    Körner, J.: Coding of an information source having ambiguous alphabet and the entropy of graphs. In: 6th Prague Conference on Information Theory, pp. 411–425 (1973)Google Scholar
  87. 87.
    Holzinger, A., Ofner, B., Stocker, C., Calero Valdez, A., Schaar, A.K., Ziefle, M., Dehmer, M.: On graph entropy measures for knowledge discovery from publication network data. In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 354–362. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40511-2_25 CrossRefGoogle Scholar
  88. 88.
    Dehmer, M.: Information theory of networks. Symmetry 3, 767–779 (2011)MathSciNetCrossRefGoogle Scholar
  89. 89.
    Adler, R.L., Konheim, A.G., McAndrew, M.H.: Topological entropy. Trans. Am. Math. Soc. 114, 309–319 (1965)MathSciNetCrossRefMATHGoogle Scholar
  90. 90.
    Adler, R., Downarowicz, T., Misiurewicz, M.: Topological entropy. Scholarpedia 3, 2200 (2008)CrossRefGoogle Scholar
  91. 91.
    Hornero, R., Aboy, M., Abasolo, D., McNames, J., Wakeland, W., Goldstein, B.: Complex analysis of intracranial hypertension using approximate entropy. Crit. Care Med. 34, 87–95 (2006)CrossRefGoogle Scholar
  92. 92.
    Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. 88, 2297–2301 (1991)MathSciNetCrossRefMATHGoogle Scholar
  93. 93.
    Mueller, H., Reihs, R., Zatloukal, K., Holzinger, A.: Analysis of biomedical data with multilevel glyphs. BMC Bioinform. 15, S5 (2014)CrossRefGoogle Scholar
  94. 94.
    Toderici, G., Aradhye, H., Paca, M., Sbaiz, L., Yagnik, J.: Finding meaning on youtube: tag recommendation and category discovery. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), pp. 3447–3454. IEEE (2010)Google Scholar
  95. 95.
    Sturm, W., Schreck, T., Holzinger, A., Ullrich, T.: Discovering medical knowledge using visual analytics a survey on methods for systems biology and omics data. In: Bühler, K., Linsen, L., John, N.W. (eds.) Eurographics Workshop on Visual Computing for Biology and Medicine, Eurographics EG, pp. 71–81 (2015)Google Scholar
  96. 96.
    Müller, E., Assent, I., Krieger, R., Jansen, T., Seidl, T.: Morpheus: interactive exploration of subspace clustering. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2008, pp. 1089–1092. ACM (2008)Google Scholar
  97. 97.
    Shepard, R.N.: The analysis of proximities: multidimensional scaling with an unknown distance function. Psychometrika 27, 125–140 (1962)MathSciNetCrossRefMATHGoogle Scholar
  98. 98.
    Duchi, J.C., Jordan, M.I., Wainwright, M.J.: Privacy aware learning. J. ACM (JACM) 61, 38 (2014)MathSciNetCrossRefMATHGoogle Scholar
  99. 99.
    Malle, B., Kieseberg, P., Weippl, E., Holzinger, A.: The right to be forgotten: towards machine learning on perturbed knowledge bases. In: Buccafurri, F., Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-ARES 2016. LNCS, vol. 9817, pp. 251–266. Springer, Heidelberg (2016). doi:10.1007/978-3-319-45507-5_17 CrossRefGoogle Scholar
  100. 100.
    Bloice, M.D., Holzinger, A.: A tutorial on machine learning and data science tools with python. In: Holzinger, A. (ed.) ML for Health Informatics. LNCS (LNAI), vol. 9605, pp. 435–480. Springer, Heidelberg (2016)Google Scholar
  101. 101.
    Gordon, A.D., Henzinger, T.A., Nori, A.V., Rajamani, S.K.: Probabilistic programming. In: Proceedings of the on Future of Software Engineering, pp. 167–181. ACM (2014)Google Scholar
  102. 102.
    Jeanquartier, F., Jean-Quartier, C., Kotlyar, M., Tokar, T., Hauschild, A.C., Jurisica, I., Holzinger, A.: Machine learning for in silico modeling of tumor growth. In: Holzinger, A. (ed.) ML for Health Informatics. LNCS (LNAI), vol. 9605, pp. 415–434. Springer, Heidelberg (2016)Google Scholar
  103. 103.
    Valiant, L.G.: A theory of the learnable. Commun. ACM 27, 1134–1142 (1984)CrossRefMATHGoogle Scholar
  104. 104.
    Baxter, J.: A model of inductive bias learning. J. Artif. Intell. Res. 12, 149–198 (2000)MathSciNetMATHGoogle Scholar
  105. 105.
    Evgeniou, T., Pontil, M.: Regularized multi-task learning. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 109–117. ACM (2004)Google Scholar
  106. 106.
    Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009)MATHGoogle Scholar
  107. 107.
    Parameswaran, S., Weinberger, K.Q.: Large margin multi-task metric learning. In: Lafferty, J., Williams, C., Shawe-Taylor, J., Zemel, R., Culotta, A. (eds.) Advances in Neural Information Processing Systems (NIPS 2010), vol. 23, pp. 1867–1875 (2010)Google Scholar
  108. 108.
    McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: the sequential learning problem. In: Bower, G.H. (ed.) The Psychology of Learning and Motivation, vol. 24, pp. 109–164. Academic Press, San Diego (1989)Google Scholar
  109. 109.
    French, R.M.: Catastrophic forgetting in connectionist networks. Trends Cogn. Sci. 3, 128–135 (1999)CrossRefGoogle Scholar
  110. 110.
    Goodfellow, I.J., Mirza, M., Xiao, D., Courville, A., Bengio, Y.: An empirical investigation of catastrophic forgeting in gradient-based neural networks arXiv:1312.6211v3 (2015)
  111. 111.
    Pan, S.J., Yang, Q.A.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359 (2010)CrossRefGoogle Scholar
  112. 112.
    Taylor, M.E., Stone, P.: Transfer learning for reinforcement learning domains: a survey. J. Mach. Learn. Res. 10, 1633–1685 (2009)MathSciNetMATHGoogle Scholar
  113. 113.
    Sycara, K.P.: Multiagent systems. AI Mag. 19, 79 (1998)Google Scholar
  114. 114.
    Lynch, N.A.: Distributed Algorithms. Morgan Kaufmann, San Francisco (1996)MATHGoogle Scholar
  115. 115.
    DeGroot, M.H.: Reaching a consensus. J. Am. Stat. Assoc. 69, 118–121 (1974)CrossRefMATHGoogle Scholar
  116. 116.
    Benediktsson, J.A., Swain, P.H.: Consensus theoretic classification methods. IEEE Trans. Syst. Man Cybern. 22, 688–704 (1992)CrossRefMATHGoogle Scholar
  117. 117.
    Weller, S.C., Mann, N.C.: Assessing rater performance without a gold standard using consensus theory. Med. Decis. Making 17, 71–79 (1997)CrossRefGoogle Scholar
  118. 118.
    Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95, 215–233 (2007)CrossRefGoogle Scholar
  119. 119.
    Roche, B., Guegan, J.F., Bousquet, F.: Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission. BMC Bioinform. 9, 435 (2008)CrossRefGoogle Scholar
  120. 120.
    Kok, J.R., Vlassis, N.: Collaborative multiagent reinforcement learning by payoff propagation. J. Mach. Learn. Res. 7, 1789–1828 (2006)MathSciNetMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Holzinger Group, HCI-KDD, Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria
  2. 2.Institute for Information Systems and Computer MediaGraz University of TechnologyGrazAustria

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