Skip to main content
SpringerLink
Log in

Data Visualization in Clinical Practice

  • Chapter
  • First Online:
Data Science for Healthcare

Abstract

As health-care data is increasingly digitized and standardized not only for research purposes but also for clinical practice, opportunities for increased personalized medicine through big data analytics arise. However, practical limitations exist towards acceptance of data analytics models to be used in clinical practice. Traditionally, models (typically rule-based) are extensively validated before being taken up in practice. With the fast pace of development of new data, techniques, and devices, time-consuming external validation will often invalidate future application of a model, due to new or better diagnostic measurements or treatment techniques.

To accommodate for this fast pace of development, a more flexible way of model development is needed. This entails that certain levels of uncertainty need to be accepted in the external validity of the model, either because the model has not undergone thorough external validation or because circumstances have changed since the model was developed.

We can allow for the doctor to stay in charge of any inferences made from data through visualization instead of mere presentation of, e.g., risk scores or survival probabilities from a trained model. Absence of external validation requires that visualizations are easily interpretable: it should be clear how they were constructed (they should be as unbiased as possible), and the limitations of the underlying model of the data should be clearly presented to the user.

In this chapter, we present direct data visualization techniques, which adhere to these requirements, along with their limitations and directions for future research into readily interpretable, unbiased data visualizations for big data in health care.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abernethy, A.P., Etheredge, L.M., Ganz, P.A., Wallace, P., German, R.R., Neti, C., Bach, P.B., Murphy, S.B.: Rapid-learning system for cancer care. J. Clin. Oncol. 28(27), 4268–4274 (2010)

    Article  Google Scholar 

  2. Acharyya, R.: A New Approach for Blind Source Separation of Convolutive Sources. VDM Verlag, Saarbrucken (2008)

    Google Scholar 

  3. Alpaydin, E.: Introduction to Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, Cambridge (2009)

    MATH  Google Scholar 

  4. Assaad, H.E., Same’, A., Govaert, G., Aknin, P.: A variational expectation-maximization algorithm for temporal data clustering. Comput. Stat. Data Anal. 103, 206–228 (2016)

    Google Scholar 

  5. Bojanowski, M., Edwards, R.: Alluvial: R Package for Creating Alluvial Diagrams (2016). r package version: 0.1–2 https://github.com/mbojan/alluvial

  6. Box, G.E.P.: Science and statistics. J. Am. Stat. Assoc. 71(356), 791–799 (1976)

    Article  MathSciNet  Google Scholar 

  7. Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1721–1730. ACM, New York (2015)

    Google Scholar 

  8. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)

    Article  Google Scholar 

  9. Dhillon, I.S., Modha, D.S.: Concept decompositions for large sparse text data using clustering. Mach. Learn. 42, 143–175 (2001)

    Article  Google Scholar 

  10. Driver, J.: A selective review of selective attention research from the past century. Br. J. Psychol. 92(1), 53–78 (2001)

    Article  Google Scholar 

  11. Everitt, B.: Cluster Analysis. Wiley, Chichester (2011)

    Book  Google Scholar 

  12. Everitt, B.S.: An Introduction to Latent Variables Models. Chapman & Hall/CRC Press, Boca Raton (1984)

    Book  Google Scholar 

  13. Goldstraw, P., Crowley, J., Chansky, K., Giroux, D.J., Groome, P.A., Rami-Porta, R., Postmus, P.E., Rusch, V., Sobin, L., for the Study of Lung Cancer International Staging Committee IA, et al.: The IASLC lung cancer staging project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the tnm classification of malignant tumours. J. Thoracic Oncol. 2(8), 706–714 (2007)

    Google Scholar 

  14. Heinrich, J., Weiskopf, D.: State of the art of parallel coordinates. In: Eurographics (STARs), pp. 95–116 (2013)

    Google Scholar 

  15. Hinich, V., Vaintrob, A.: Cyclic operads and algebra of chord diagrams. Sel. Math. 8(2), 237–282 (2002)

    Article  MathSciNet  Google Scholar 

  16. Holmes, G., Donkin, A., Witten, I.H.: Weka: a machine learning workbench. In: Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, pp. 357–361. IEEE, Piscataway (1994)

    Google Scholar 

  17. Ihaka, R., Gentleman, R.: R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5(3), 299–314 (1996)

    Google Scholar 

  18. Jesus, J., Chandler, R.E.: Estimating functions and the generalized method of moments. Interface Focus 1(6), 871–885 (2011)

    Article  Google Scholar 

  19. Jolliffe, I.: Principal Component Analysis, 2nd edn. Springer, New York (2002)

    MATH  Google Scholar 

  20. Kang, J., Schwartz, R., Flickinger, J., Beriwal, S.: Machine learning approaches for predicting radiation therapy outcomes: a clinician’s perspective. Int. J. Radiat. Oncol. Biol. Phys. 93(5), 1127–1135 (2015)

    Article  Google Scholar 

  21. Keysers, C., Perrett, D.I.: Demystifying social cognition: a Hebbian perspective. Trends Cogn. Sci. 8(11), 501–507 (2004)

    Article  Google Scholar 

  22. Koffka, K.: Principles of Gestalt Psychology, vol. 44. Routledge, Abingdon (2013)

    Book  Google Scholar 

  23. Kosara, R., Bendix, F., Hauser, H.: Parallel sets: interactive exploration and visual analysis of categorical data. IEEE Trans. Vis. Comput. Graph. 12(4), 558–568 (2006)

    Article  Google Scholar 

  24. Krumholz, H.M.: Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Aff. 33(7), 1163–1170 (2014)

    Article  Google Scholar 

  25. Li, L., Zhang, Y.J.: Survey on algorithms of non-negative matrix factorization. Tien Tzu Hsueh Pao/Acta Electron. Sin. 36(4), 737–743 (2008)

    Google Scholar 

  26. Liang, W., Zhang, L., Jiang, G., Wang, Q., Liu, L., Liu, D., Wang, Z., Zhu, Z., Deng, Q., Xiong, X., Shao, W., Shi, X., He, J.: Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 33(8), 861–869 (2015)

    Article  Google Scholar 

  27. Miller, G.A.: The magic number seven plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 91–97 (1956)

    Article  Google Scholar 

  28. Millman, K.J., Aivazis, M.: Python for scientists and engineers. Comput. Sci. Eng. 13(2), 9–12 (2011)

    Article  Google Scholar 

  29. Modha, D.S., Spangler, W.S.: Feature weighting in k-means clustering. J. Mach. Learn. 52, 217–237 (2001)

    Article  Google Scholar 

  30. Mottet, N., Bellmunt, J., Bolla, M., Briers, E., Cumberbatch, M.G., De Santis, M., Fossati, N., Gross, T., Henry, A.M., Joniau, S., et al.: Eau-estro-siog guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent. Eur. Urol. 71(4), 618–629 (2017)

    Article  Google Scholar 

  31. Nie, N.H., Bent, D.H., Hull, C.H.: SPSS: statistical package for the social sciences. Tech. rep., McGraw-Hill, New York (1970)

    Google Scholar 

  32. Peng, G., Hakim, M., Broza, Y.Y., Billan, S., Abdah-Bortnyak, R., Kuten, A., Tisch, U., Haick, H.: Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. Br. J. Cancer 103(4), 542 (2010)

    Article  Google Scholar 

  33. Peysakhovich, V., Hurter, C., Telea, A.: Attribute-driven edge bundling for general graphs with applications in trail analysis. In: 2015 IEEE Pacific Visualization Symposium, PacificVis 2015, Hangzhou, 14–17 April, pp. 39–46 (2015)

    Google Scholar 

  34. Posner, M.I., Nissen, M.J., Klein, R.M.: Visual dominance: an information-processing account of its origins and significance. Psychol. Rev. 83(2), 157 (1976)

    Article  Google Scholar 

  35. Ripley, B.: Pattern Recognition and Neural Networks. Cambridge University Press, New York (2007)

    MATH  Google Scholar 

  36. Saaty, T.L.: Relative measurement and its generalization in decision making why pairwise comparisons are central in mathematics for the measurement of intangible factors the analytic hierarchy/network process. Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales Serie A Matematicas 102(2), 251–318 (2008)

    Article  MathSciNet  Google Scholar 

  37. Trevor, R.T., Friedman, J.: The Elements of Statistical Learning: Data mining, Inference, and Prediction. Springer, New York (2009)

    MATH  Google Scholar 

  38. Wang, L., Mullerad, M., Chen, H.N., Eberhardt, S.C., Kattan, M.W., Scardino, P.T., Hricak, H.: Prostate cancer: incremental value of endorectal MR imaging findings for prediction of extracapsular extension. Radiology 232(1), 133–139 (2004)

    Article  Google Scholar 

  39. Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., Wang, F.Y.: Generative adversarial networks: introduction and outlook. IEEE/CAA J. Autom. Sin. 4(4), 588–598 (2017)

    Article  MathSciNet  Google Scholar 

  40. Wickens, C., Lee, J., Liu, Y., Gordon-Becker, S.E.: Designing for People: An Introduction to Human Factors Engineering, 3rd edn. CreateSpace, Charleston (2018)

    Google Scholar 

  41. Wilson, P.W., D’Agostino, R.B., Levy, D., Belanger, A.M., Silbershatz, H., Kannel, W.B.: Prediction of coronary heart disease using risk factor categories. Circulation 97(18), 1837–1847 (1998)

    Article  Google Scholar 

  42. Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1–3), 37–52 (1987)

    Article  Google Scholar 

  43. Wu, B., Ricchetti, F., Sanguineti, G., Kazhdan, M., Simari, P., Jacques, R., Taylor, R., McNutt, T.: Data-driven approach to generating achievable dose-volume histogram objectives in intensity-modulated radiotherapy planning. Int. J. Radiat. Oncol. Biol. Phys. 79(4), 1241–1247 (2011)

    Article  Google Scholar 

  44. Yang, J., Peng, W., Ward, M.O., Rundensteiner, E.A.: Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. In: IEEE Symposium on Information Visualization, INFOVIS 2003, pp. 105–112. IEEE, Piscataway (2003)

    Google Scholar 

  45. Yousefi, S., Amrollahi, F., Amgad, M., Dong, C., Lewis, J.E., Song, C., Gutman, D.A., Halani, S.H., Vega, J.E.V., Brat, D.J., et al.: Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Sci. Rep. 7(1), 11707 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Philips Research, Eindhoven, The Netherlands

    Monique Hendriks, Charalampos Xanthopoulakis, Pieter Vos, Sergio Consoli & Jacek Kustra

Authors
  1. Monique Hendriks
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charalampos Xanthopoulakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pieter Vos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sergio Consoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jacek Kustra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Monique Hendriks .

Editor information

Editors and Affiliations

  1. Philips Research, Eindhoven, The Netherlands

    Sergio Consoli

  2. Dept of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy

    Diego Reforgiato Recupero

  3. Data Science Department, Philips Research, Eindhoven, The Netherlands

    Milan Petković

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hendriks, M., Xanthopoulakis, C., Vos, P., Consoli, S., Kustra, J. (2019). Data Visualization in Clinical Practice. In: Consoli, S., Reforgiato Recupero, D., Petković, M. (eds) Data Science for Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-030-05249-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05249-2_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05248-5

  • Online ISBN: 978-3-030-05249-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation