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

Open access

Abstract

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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 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 Applications 10(8): 261–270.

  • 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.

  • Castanedo F. (2013). A review of data fusion techniques The Scientific World Journal 2013: 704504 DOI: 10.1155/2013/704504.

  • 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 Surgery 137(5): 625–630.

  • de Bruijne M. (2016). Machine learning approaches in medical image analysis: From detection to diagnosis Medical Image Analysis 33: 94–97 DOI: 10.106/j.media.2016.06.032.

  • 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.

  • 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.

  • 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 Engineering 4(3): 479–482.

  • Ferri C. Hernndez-Orallo J. and Modroiu R. (2009). An experimental comparison of performance measures for classification Pattern Recognition Letters 30(1): 27–38.

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

  • 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 Newsletter 11(1): 10–18.

  • 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.

  • Hossain M. Neelapala V. and Andrew J.G. (2008). Results of non-operative treatment following hip fracture compared to surgical intervention Injury 40(4): 418–421.

  • 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 Physics 33(8): 2945–2954 DOI: 10.1118/1.2208934.

  • Khatik I. (2017). A study of various bone fracture detection techniques International Journal of Engineering and Computer Science 6(5): 21418–21423.

  • 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 Journal 13: 8–17.

  • Koziarski M. and Woźniak M. (2017). CCR: A combined cleaning and resampling algorithm for imbalanced data classification International Journal of Applied Mathematics and Computing Sciences 27(4): 727–736 DOI: 10.1515/amcs-2017-0050.

  • Kuhn M. and Johnson K. (2013). Applied Predictive Modeling Springer New York NY.

  • Lahat D. Adali T. and Jutten C. (2015). Multimodal data fusion: An overview of methods challenges and prospects Proceedings of the IEEE 103(9): 1449–1477.

  • 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.

  • 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.

  • Mitchell H.B. (2014). Data Fusion: Concepts and Ideas Springer Berlin/Heidelberg.

  • 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.

  • 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.

  • Salzberg S.L. and Fayyad U. (1997). On comparing classifiers: Pitfalls to avoid and a recommended approach Data Mining and Knowledge Discovery 1(3): 317–328 DOI: 10.1023/A:1009752403260.

  • 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 ONE 12(2): 1–15 DOI:10.1371/journal.pone.0173021.

  • 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.

  • 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 Imaging 17(1): 2.

  • 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.

  • Yuksel S.E. Wilson J.N. and Gader P.D. (2012). Twenty years of mixture of experts IEEE Transactions on Neural Networks and Learning Systems 23(8): 1177–1193.

  • Zorluoglu G. and Agaoglu M. (2015). Diagnosis of breast cancer using ensemble of data mining classification methods International Journal of Bioinformatics and Biomedical Engineering 1(3): 318–322.

Search
Journal information
Impact Factor

IMPACT FACTOR 2018: 1.504
5-year IMPACT FACTOR: 1.553

CiteScore 2018: 2.09

SCImago Journal Rank (SJR) 2018: 0.493
Source Normalized Impact per Paper (SNIP) 2018: 1.361

Mathematical Citation Quotient (MCQ) 2018: 0.08

Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 189 189 7
PDF Downloads 165 165 9