Data Mining in Medicine

Clinical databases collect large volumes of information. Relationships and patterns within these data could provide new medical knowledge. Data mining has as major objective the discovery of knowledge from large amounts of data, offers many possibilities for identifying different data features less visible or hidden to common analysis techniques. This chapter focuses on a selection of techniques and illustrates their applicability to medical diagnostic and prognostic problems.

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References

  1. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AI magazine, vol. 17, no. 3, pp. 37–37, 1996. Google Scholar
  2. N. Jothi, W. Husain, et al., “Data mining in healthcare–a review,” Procedia Computer Science, vol. 72, pp. 306–313, 2015. ArticleGoogle Scholar
  3. O. Maimon and L. Rokach, “Introduction to knowledge discovery and data mining,” in Data mining and knowledge discovery handbook, pp. 1–15, Springer, 2009. Google Scholar
  4. R. Bellazzi and B. Zupan, “Predictive data mining in clinical medicine: current issues and guidelines,” International journal of medical informatics, vol. 77, no. 2, pp. 81–97, 2008. ArticleGoogle Scholar
  5. C. Robert, “Machine learning, a probabilistic perspective,” 2014. Google Scholar
  6. J.-G. Lee, S. Jun, Y.-W. Cho, H. Lee, G. B. Kim, J. B. Seo, and N. Kim, “Deep learning in medical imaging: general overview,” Korean journal of radiology, vol. 18, no. 4, pp. 570–584, 2017. ArticleGoogle Scholar
  7. S. K. Pandey and R. R. Janghel, “Recent deep learning techniques, challenges and its applications for medical healthcare system: A review,” Neural Processing Letters, pp. 1–29, 2019. Google Scholar
  8. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, p. 436, 2015. Google Scholar
  9. L. Deng, “A tutorial survey of architectures, algorithms, and applications for deep learning,” APSIPA Transactions on Signal and Information Processing, vol. 3, 2014. Google Scholar
  10. R. Miotto, F. Wang, S. Wang, X. Jiang, and J. T. Dudley, “Deep learning for healthcare: review, opportunities and challenges,” Briefings in bioinformatics, vol. 19, no. 6, pp. 1236–1246, 2017. ArticleGoogle Scholar
  11. A. A. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. Van Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. van Ginneken, “Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks,” IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1160–1169, 2016. ArticleGoogle Scholar
  12. H. R. Roth, L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim, and R. M. Summers, “Improving computer-aided detection using convolutional neural networks and random view aggregation,” IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1170–1181, 2015. ArticleGoogle Scholar
  13. Q. Dou, H. Chen, L. Yu, L. Zhao, J. Qin, D. Wang, V. C. Mok, L. Shi, and P.-A. Heng, “Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks,” IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1182–1195, 2016. ArticleGoogle Scholar
  14. K. Sirinukunwattana, S. E. A. Raza, Y.-W. Tsang, D. R. Snead, I. A. Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images,” IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1196–1206, 2016. ArticleGoogle Scholar
  15. G. Currie, K. E. Hawk, E. Rohren, A. Vial, and R. Klein, “Machine learning and deep learning in medical imaging: Intelligent imaging,” Journal of Medical Imaging and Radiation Sciences, vol. 50, p. 477–487, Dec 2019. ArticleGoogle Scholar
  16. A. T. Kharroubi and H. M. Darwish, “Diabetes mellitus: The epidemic of the century,” World journal of diabetes, vol. 6, no. 6, p. 850, 2015. Google Scholar
  17. T. Y. Wong, C. M. G. Cheung, M. Larsen, S. Sharma, and R. Simó, “Diabetic retinopathy,” Nature Reviews Disease Primers, vol. 2, 2016. Google Scholar
  18. V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” Jama, vol. 316, no. 22, pp. 2402–2410, 2016. ArticleGoogle Scholar
  19. M. D. Abràmoff, P. T. Lavin, M. Birch, N. Shah, and J. C. Folk, “Pivotal trial of an autonomous ai-based diagnostic system for detection of diabetic retinopathy in primary care offices,” NPJ Digital Medicine, vol. 1, no. 1, p. 39, 2018. Google Scholar
  20. N. Harbeck, F. Penault-Llorca, J. Cortes, M. Gnant, N. Houssami, P. Poortmans, K. Ruddy, J. Tsang, and F. Cardoso, “Breast cancer,” Nature Reviews Disease Primers, vol. 5, Sep 2019. Google Scholar
  21. B. E. Bejnordi, M. Veta, P. J. Van Diest, B. Van Ginneken, N. Karssemeijer, G. Litjens, J. A. Van Der Laak, M. Hermsen, Q. F. Manson, M. Balkenhol, et al., “Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer,” Jama, vol. 318, no. 22, pp. 2199–2210, 2017. ArticleGoogle Scholar
  22. J. Wang, X. Yang, H. Cai, W. Tan, C. Jin, and L. Li, “Discrimination of breast cancer with microcalcifications on mammography by deep learning,” Scientific reports, vol. 6, no. 1, pp. 1–9, 2016. Google Scholar
  23. A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, p. 115, 2017. Google Scholar
  24. M. H. Jafari, N. Karimi, E. Nasr-Esfahani, S. Samavi, S. M. R. Soroushmehr, K. Ward, and K. Najarian, “Skin lesion segmentation in clinical images using deep learning,” in 2016 23rd International conference on pattern recognition (ICPR), pp. 337–342, IEEE, 2016. Google Scholar
  25. Y. Cheng, F. Wang, P. Zhang, and J. Hu, “Risk prediction with electronic health records: A deep learning approach,” in Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 432–440, SIAM, 2016. Google Scholar
  26. A. Avati, K. Jung, S. Harman, L. Downing, A. Ng, and N. H. Shah, “Improving palliative care with deep learning,” BMC medical informatics and decision making, vol. 18, no. 4, p. 122, 2018. Google Scholar
  27. A. Rajkomar, E. Oren, K. Chen, A. M. Dai, N. Hajaj, M. Hardt, P. J. Liu, X. Liu, J. Marcus, M. Sun, et al., “Scalable and accurate deep learning with electronic health records,” NPJ Digital Medicine, vol. 1, no. 1, p. 18, 2018. Google Scholar
  28. J. A. Golden, “Deep learning algorithms for detection of lymph node metastases from breast cancer: helping artificial intelligence be seen,” Jama, vol. 318, no. 22, pp. 2184–2186, 2017. ArticleGoogle Scholar
  29. A. R. Post, A. N. Sovarel, and J. H. Harrison Jr, “Abstraction-based temporal data retrieval for a clinical data repository,” in AMIA Annual Symposium Proceedings, vol. 2007, p. 603, American Medical Informatics Association, 2007. Google Scholar
  30. C. Combi, M. Mantovani, and P. Sala, “Discovering quantitative temporal functional dependencies on clinical data,” in 2017 IEEE International Conference on Healthcare Informatics (ICHI), pp. 248–257, IEEE, 2017. Google Scholar
  31. A. Shknevsky, Y. Shahar, and R. Moskovitch, “Consistent discovery of frequent interval-based temporal patterns in chronic patients’ data,” Journal of biomedical informatics, vol. 75, pp. 83–95, 2017. ArticleGoogle Scholar
  32. R. Moskovitch and Y. Shahar, “Fast time intervals mining using the transitivity of temporal relations,” Knowledge and Information Systems, vol. 42, no. 1, pp. 21–48, 2015. ArticleGoogle Scholar
  33. C. Combi, E. Keravnou-Papailiou, and Y. Shahar, Temporal information systems in medicine. Springer Science & Business Media, 2010. Google Scholar
  34. R. Moskovitch and Y. Shahar, “Classification-driven temporal discretization of multivariate time series,” Data Mining and Knowledge Discovery, vol. 29, no. 4, pp. 871–913, 2015. ArticleMathSciNetGoogle Scholar
  35. R. Moskovitch and Y. Shahar, “Classification of multivariate time series via temporal abstraction and time intervals mining,” Knowledge and Information Systems, vol. 45, no. 1, pp. 35–74, 2015. ArticleGoogle Scholar
  36. Y. Shahar, “A framework for knowledge-based temporal abstraction,” Artificial intelligence, vol. 90, no. 1-2, pp. 79–133, 1997. ArticleMATHGoogle Scholar
  37. Y. Shahar and M. A. Musen, “Knowledge-based temporal abstraction in clinical domains,” Artificial intelligence in medicine, vol. 8, no. 3, pp. 267–298, 1996. ArticleGoogle Scholar
  38. E. Sheetrit, N. Nissim, D. Klimov, and Y. Shahar, “Temporal probabilistic profiles for sepsis prediction in the ICU,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2961–2969, 2019. Google Scholar
  39. S. Concaro, L. Sacchi, C. Cerra, P. Fratino, and R. Bellazzi, “Mining healthcare data with temporal association rules: Improvements and assessment for a practical use,” in Conference on Artificial Intelligence in Medicine in Europe, pp. 16–25, Springer, 2009. Google Scholar
  40. L. Sacchi, C. Larizza, C. Combi, and R. Bellazzi, “Data mining with temporal abstractions: learning rules from time series,” Data Mining and Knowledge Discovery, vol. 15, no. 2, pp. 217–247, 2007. ArticleMathSciNetGoogle Scholar
  41. R. Bellazzi, C. Larizza, and A. Riva, “Temporal abstractions for interpreting diabetic patients monitoring data,” Intelligent Data Analysis, vol. 2, no. 1–4, pp. 97–122, 1998. ArticleGoogle Scholar
  42. J. F. Allen, “Towards a general theory of action and time,” Artificial intelligence, vol. 23, no. 2, pp. 123–154, 1984. ArticleMATHGoogle Scholar
  43. C. Combi and A. Sabaini, “Extraction, analysis, and visualization of temporal association rules from interval-based clinical data,” in Conference on Artificial Intelligence in Medicine in Europe, pp. 238–247, Springer, 2013. Google Scholar
  44. M. Mantovani, C. Combi, and M. Zeggiotti, “Discovering and analyzing trend-event patterns on clinical data,” 2019 IEEE International Conference on Healthcare Informatics (ICHI), pp. 1–10, 2019. Google Scholar
  45. R. Bellazzi, C. Larizza, P. Magni, and R. Bellazzi, “Temporal data mining for the quality assessment of hemodialysis services,” Artificial intelligence in medicine, vol. 34, no. 1, pp. 25–39, 2005. ArticleGoogle Scholar
  46. C. Combi, A. Montanari, and P. Sala, “A uniform framework for temporal functional dependencies with multiple granularities,” in International Symposium on Spatial and Temporal Databases, pp. 404–421, Springer, 2011. Google Scholar
  47. C. Combi, A. Montanari, and G. Pozzi, “The T4SQL temporal query language,” in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pp. 193–202, ACM, 2007. Google Scholar
  48. J. Kivinen and H. Mannila, “Approximate inference of functional dependencies from relations,” Theoretical Computer Science, vol. 149, no. 1, pp. 129–149, 1995. ArticleMathSciNetMATHGoogle Scholar
  49. C. Combi, M. Franceschet, and A. Peron, “Representing and reasoning about temporal granularities,” Journal of Logic and Computation, vol. 14, no. 1, pp. 51–77, 2004. ArticleMathSciNetMATHGoogle Scholar
  50. C. Combi, M. Mantovani, A. Sabaini, P. Sala, F. Amaddeo, U. Moretti, and G. Pozzi, “Mining approximate temporal functional dependencies with pure temporal grouping in clinical databases,” Computers in biology and medicine, vol. 62, pp. 306–324, 2015. ArticleGoogle Scholar

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Verona, Verona, Italy Beatrice Amico & Carlo Combi
  2. Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beersheba, Israel Yuval Shahar
  1. Beatrice Amico