Fusion of Clinical Data: A Case Study to Predict the Type of Treatment of Bone Fractures
Clinical data is characterized not only by its constantly increasing volume but also by its diversity. Information collected in clinical information systems such as electronic health records is highly heterogeneous and it includes structured laboratory and examination reports, unstructured clinical notes, images, and more often genetic data. This heterogeneity poses a significant challenge when constructing diagnostic and therapeutic decision models that should use data from all available sources to provide a comprehensive support. A possible response to this challenge is offered by the concept of data fusion and associated techniques. In this paper, we briefly describe the foundations of data fusion and present its application in a case study aimed at building a decision model to predict the type of treatment for patients with bone fractures. Specifically, the model should distinguish between patients who should undergo a surgery and those who should be treated non-surgically.
KeywordsClinical data Data fusion Prediction models Decision support
The second author would like to acknowledge support by the Polish National Science Center under Grant No. DEC-2013/11/B/ST6/00963.
- 3.Castebedo, F.: A review of data fusion techniques. Sci. World J. (2013). doi: 10.1155/2013/704504
- 4.Rohlfing, T., Pfefferbaum, A., Sullivan, E.V., Maurer, C.R.: Information fusion in biomedical image analysis: combination of data vs. combination of interpretations. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 150–161. Springer, Heidelberg (2005). doi: 10.1007/11505730_13 CrossRefGoogle Scholar
- 5.Ponti Jr., M.P: Combining classifiers: from the creation of ensembles to the decision fusion. In: 24th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (2011)Google Scholar
- 6.Lee, G., Madabhushi, A.: A knowledge representation framework for integration, classification of multi-scale imaging and non-imaging data: preliminary results in predicting prostate cancer recurrence by fusing mass spectrometry and histology. In: International Symposium on Biomedical Imaging: From Nano to Macro. IEEE (2009)Google Scholar
- 8.Lanckriet, G., Deng, M., Cristianini, N., Jordan, M., Noble, W.: Kernel-based data fusion and its application to protein function prediction in yeast. In: Proceedings of Pacific Symposium on Biocomputing (2004)Google Scholar
- 11.Zorluoglu, G.M.: Diagnosis of breast cancer using ensemble of data mining classification methods. Int. J. Bioinform Biomed. Eng. 1(3), 318–322 (2015). doi: 10.5829/idosi.wasj.2014.29.dmsct.4
- 13.Myint, S., Khaing, A.S., Tun, H.M.: Detecting leg bone fracture in x-ray images. Int. J. Sci. Technol. Res. 5, 140–144 (2016)Google Scholar
- 14.Wilk, S., Stefanowski, J., Wojciechowski, S., Farion, K.J., Michalowski, W.: Application of preprocessing methods to imbalanced clinical data: an experimental study. In: Piętka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) Information Technologies in Medicine. AISC, vol. 471, pp. 503–515. Springer, Cham (2016). doi: 10.1007/978-3-319-39796-2_41 Google Scholar
- 15.Kubat, M., Matwin, S.: Addresing the curse of imbalanced training sets: one-side selection. In: Proceedings of the 14th International Conference, ICML 1997, pp. 179–186 (1997)Google Scholar
- 16.Tiwari, P., Viswanath, S., Lee, G., Madabhushi, A.: Multi-model data fusion schemes for integrated classification of imaging and non-imaging biomedical data. In: International Symposium on Biomedical Imaging: From Nano to Macro. IEEE (2011). doi: 10.1109/ISBI.2011.5872379