1. bookVolume 29 (2019): Issue 1 (March 2019)
    Exploring Complex and Big Data (special section, pp. 7-91), Johann Gamper, Robert Wrembel (Eds.)
Journal Details
License
Format
Journal
First Published
05 Apr 2007
Publication timeframe
4 times per year
Languages
English
access type Open Access

Fusion of clinical data: A case study to predict the type of treatment of bone fractures

Published Online: 29 Mar 2019
Page range: 51 - 67
Received: 19 Mar 2018
Accepted: 06 Dec 2018
Journal Details
License
Format
Journal
First Published
05 Apr 2007
Publication timeframe
4 times per year
Languages
English

A prominent characteristic of clinical data is their heterogeneity—such data include structured examination records and laboratory results, unstructured clinical notes, raw and tagged images, and genomic data. This heterogeneity poses a formidable challenge while constructing diagnostic and therapeutic decision models that are currently based on single modalities and are not able to use data in different formats and structures. This limitation may be addressed using data fusion methods. In this paper, we describe a case study where we aimed at developing data fusion models that resulted in various therapeutic decision models for predicting the type of treatment (surgical vs. non-surgical) for patients with bone fractures. We considered six different approaches to integrate clinical data: one fusion model based on combination of data (COD) and five models based on combination of interpretation (COI). Experimental results showed that the decision model constructed following COI fusion models is more accurate than decision models employing COD. Moreover, statistical analysis using the one-way ANOVA test revealed that there were two groups of constructed decision models, each containing the set of three different models. The results highlighted that the behavior of models within a group can be similar, although it may vary between different groups.

Keywords

Al-Ayyoub, M. and Al-Zghool, D. (2014). Determining the type of long bone fractures in X-ray images, WSEAS Transactions on Information Science and Applications10(8): 261–270.Search in Google Scholar

Brzezinski, J., Kosiedowski, M., Mazurek, C., Slowinski, K., Slowinski, R., Stroinski, M. and Weglarz, J. (2013). Towards telemedical centers: Digitization of inter-professional communication in healthcare, in M. Cruz-Cunha et al. (Eds.), Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care, IGI Global, Hershey, PA, pp. 805–829.Search in Google Scholar

Castanedo, F. (2013). A review of data fusion techniques, The Scientific World Journal2013: 704504, DOI: 10.1155/2013/704504.Search in Google Scholar

Cha, Y.-H., Ha, Y.-C., Yoo, J.-I., Min, Y.-S., Lee, Y.-K. and Koo, K.-H. (2017). Effect of causes of surgical delay on early and late mortality in patients with proximal hip fracture, Archives of Orthopaedic and Trauma Surgery137(5): 625–630.Search in Google Scholar

de Bruijne, M. (2016). Machine learning approaches in medical image analysis: From detection to diagnosis, Medical Image Analysis33: 94–97, DOI: 10.106/j.media.2016.06.032.Search in Google Scholar

Dittman, D.J., Khoshgoftaar, T.M. and Napolitano, A. (2014). Selecting the appropriate data sampling approach for imbalanced and high-dimensional bioinformatics datasets, IEEE 14th International Conference on Bioinformatics and Bioengineering, BIBE 2014, Boca Raton, FL, USA, pp. 304–310.Search in Google Scholar

Douali, N. and Jaulent, M. (2012). Genomic and personalized medicine decision support system, 2012 IEEE International Conference on Complex Systems (ICCS), Agadir, Morocco, pp. 1–4.Search in Google Scholar

Edward, C.P. and Hepzibah, H. (2015). A robust approach for detection of the type of fracture from X-ray images, International Journal of Advanced Research in Computer and Communication Engineering4(3): 479–482.Search in Google Scholar

Ferri, C., Hernndez-Orallo, J. and Modroiu, R. (2009). An experimental comparison of performance measures for classification, Pattern Recognition Letters30(1): 27–38.Search in Google Scholar

Giddins, G.E.B. (2015). The non-operative management of hand fractures, Journal of Hand Surgery (European Volume)40(1): 33–41.Search in Google Scholar

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I.H. (2009). The WEKA data mining software: An update, ACM SIGKDD Explorations Newsletter11(1): 10–18.Search in Google Scholar

Haq, A. and Wilk, S. (2017). Fusion of clinical data: A case study to predict the type of treatment of bone fractures, in M. Kirikova et al. (Eds.), New Trends in Databases and Information Systems, Springer, Cham, pp. 294–301.Search in Google Scholar

Hossain, M., Neelapala, V. and Andrew, J.G. (2008). Results of non-operative treatment following hip fracture compared to surgical intervention, Injury40(4): 418–421.Search in Google Scholar

Jesneck, J., Nolte, L., Baker, J., Floyd, C. and Lo, J. (2006). Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis, Medical Physics33(8): 2945–2954, DOI: 10.1118/1.2208934.Search in Google Scholar

Khatik, I. (2017). A study of various bone fracture detection techniques, International Journal of Engineering and Computer Science6(5): 21418–21423.Search in Google Scholar

Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V. and Fotiadis, D.I. (2015). Machine learning applications in cancer prognosis and prediction, Computational and Structural Biotechnology Journal13: 8–17.Search in Google Scholar

Koziarski, M. and Woźniak, M. (2017). CCR: A combined cleaning and resampling algorithm for imbalanced data classification, International Journal of Applied Mathematics and Computing Sciences27(4): 727–736, DOI: 10.1515/amcs-2017-0050.10.1515/amcs-2017-0050Open DOISearch in Google Scholar

Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling, Springer, New York, NY.Search in Google Scholar

Lahat, D., Adali, T. and Jutten, C. (2015). Multimodal data fusion: An overview of methods, challenges, and prospects, Proceedings of the IEEE103(9): 1449–1477.Search in Google Scholar

Lanckriet, G., Deng, M., Cristianini, N., Jordan, M. and Noble, W. (2004). Kernel-based data fusion and its application to protein function prediction in yeast, Pacific Symposium on Biocomputing (PSB 2004), Big Island, HI, USA, pp. 300–311.Search in Google Scholar

Lee, G., Doyle, S., Monaco, J., Madabhushi, A., Feldman, M.D., Master, S.R. and Tomaszewski, J.E. (2009). 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, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, pp. 77–80.Search in Google Scholar

Mitchell, H.B. (2014). Data Fusion: Concepts and Ideas, Springer, Berlin/Heidelberg.Search in Google Scholar

Ponti, M. (2011). Combining classifiers: From the creation of ensembles to the decision fusion, 2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials, Maceio, Alagoas, Brazil, pp. 1–10.Search in Google Scholar

Rohlfing, T., Pfefferbaum, A., Sullivan, E. and Maurer, C. (2005). Information fusion in biomedical image analysis: Combination of data vs combination of interpretations, 19th International Conference on Information Processing in Medical Imaging (IPMI’05), Glenwood Springs, CO, USA, pp. 150–161.Search in Google Scholar

Salzberg, S.L. and Fayyad, U. (1997). On comparing classifiers: Pitfalls to avoid and a recommended approach, Data Mining and Knowledge Discovery1(3): 317–328, DOI: 10.1023/A:1009752403260.10.1023/A:1009752403260Open DOISearch in Google Scholar

Sim, L.L.W., Ban, K.H.K., Tan, T.W., Sethi, S.K. and Loh, T.P. (2017). Development of a clinical decision support system for diabetes care: A pilot study, PLOS ONE12(2): 1–15, DOI:10.1371/journal.pone.0173021.10.1371/journal.pone.0173021Open DOISearch in Google Scholar

Tiwari, P., Viswanath, S., Lee, G. and Madabhushi, A. (2011). Multi-modal data fusion schemes for integrated classification of imaging and non-imaging biomedical data, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, IL, USA, pp. 165–168.Search in Google Scholar

Viswanath, S.E., Tiwari, P., Lee, G. and Madabhushi, A. (2017). Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: Concepts, workflow, and use-cases, BMC Medical Imaging17(1): 2.Search in Google Scholar

Wilk, S., Stefanowski, J., Wojciechowski, S., Farion, K.J. and Michalowski, W. (2016). Application of preprocessing methods to imbalanced clinical data: An experimental study, in E. Pietka et al. (Eds.), Information Techmologies in Medicine, Springer, Berlin/Heidelberg, pp. 503–515.Search in Google Scholar

Yuksel, S.E., Wilson, J.N. and Gader, P.D. (2012). Twenty years of mixture of experts, IEEE Transactions on Neural Networks and Learning Systems23(8): 1177–1193.Search in Google Scholar

Zorluoglu, G. and Agaoglu, M. (2015). Diagnosis of breast cancer using ensemble of data mining classification methods, International Journal of Bioinformatics and Biomedical Engineering1(3): 318–322.Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo