Humanity Is Overrated. or Not. Automatic Diagnostic Suggestions by Greg, ML (Extended Abstract)

  • Paola Lapadula
  • Giansalvatore Mecca
  • Donatello SantoroEmail author
  • Luisa Solimando
  • Enzo Veltri
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 909)


This paper introduces Greg, ML, a machine-learning tool for generating automatic diagnostic suggestions based on patient profiles. We discuss the architecture that stands at the core of Greg, and some experimental results based on the working prototype we have developed. Finally, we discuss challenges and opportunities related to the use of this kind of tools in medicine, and some important lessons learned developing the tool. In this respect, despite the ironic title of this paper, we underline that Greg should be conceived primarily as a support for expert doctors in their diagnostic decisions, and can hardly replace humans in their judgment.


  1. 1.
    Deo, R.C.: Machine learning in medicine. Circulation 132(20), 1920–1930 (2015)CrossRefGoogle Scholar
  2. 2.
    Geerts, F., Mecca, G., Papotti, P., Santoro, D.: Mapping and cleaning. In: Proceedings of the IEEE International Conference on Data Engineering - ICDE (2014)Google Scholar
  3. 3.
    Geerts, F., Mecca, G., Papotti, P., Santoro, D.: That’s All Folks! LLUNATIC goes open source. In: Proceedings of the International Conference on Very Large Databases - VLDB (2014)Google Scholar
  4. 4.
    He, J., Veltri, E., Santoro, D., Li, G., Mecca, G., Papotti, P., Tang, N.: Interactive and deterministic data cleaning. In: Proceedings of the 2016 International Conference on Management of Data, SIGMOD Conference 2016, pp. 893–907 (2016)Google Scholar
  5. 5.
    Heinis, T., Ailamaki, A.: Data infrastructure for medical research. Found. Trends Databases 8(3), 131–238 (2017)CrossRefGoogle Scholar
  6. 6.
    Holzinger, A.: Machine learning for health informatics. In: Holzinger, A. (ed.) Machine Learning for Health Informatics. LNCS (LNAI), vol. 9605, pp. 1–24. Springer, Cham (2016). Scholar
  7. 7.
    Kononenko, I.: Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 23(1), 89–109 (2001)CrossRefGoogle Scholar
  8. 8.
    Miller, R.H., Sim, I.: Physicians’ use of electronic medical records: barriers and solutions. Health Aff. 23(2), 116–126 (2004)CrossRefGoogle Scholar
  9. 9.
    Mohammed, O., Benlamri, R.: Developing a semantic web model for medical differential diagnosis recommendation. J. Med. Syst. 38(10), 79 (2014)CrossRefGoogle Scholar
  10. 10.
    Peek, N., Combi, C., Marin, R., Bellazzi, R.: Thirty years of artificial intelligence in medicine (aime) conferences: a review of research themes. Artif. Intell. Med. 65(1), 61–73 (2015)CrossRefGoogle Scholar
  11. 11.
    Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., Ng, A.Y.: Cardiologist-level arrhythmia detection with convolutional neural networks (2017). arXiv preprint arXiv:1707.01836
  12. 12.
    Rajpurkar, P., et al.: Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning (2017). arXiv preprint arXiv:1711.05225
  13. 13.
    Soni, J., Ansari, U., Sharma, D., Soni, S.: Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int. J. Comput. Appl. 17(8), 43–48 (2011)Google Scholar
  14. 14.
    Steadman, I.: IBM’s Watson is better at diagnosing cancer than human doctors. In: WIRED (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Paola Lapadula
    • 1
  • Giansalvatore Mecca
    • 1
  • Donatello Santoro
    • 1
    Email author
  • Luisa Solimando
    • 2
  • Enzo Veltri
    • 2
  1. 1.Università della BasilicataPotenzaItaly
  2. 2.Svelto! Big Data Cleaning and AnalyticsPotenzaItaly

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