A comprehensive Overview of Decision Fusion Technique in Healthcare: A Systematic Scoping Review

IRCMJ logo
PDF
HTML

Keywords

Decision Fusion
Expert Fusion
Health care
Information Fusion
Medicine
Sensor Fusion

How to Cite

Nazari, E., Chang, H.-C. H., Deldar, K., Pour, R., Avan, A., Tara, M., Mehrabian, A., & tabesh, hamed. (2020). A comprehensive Overview of Decision Fusion Technique in Healthcare: A Systematic Scoping Review. Iranian Red Crescent Medical Journal, 22(10). https://doi.org/10.32592/ircmj.2020.22.10.30

Abstract

Introduction: Decision fusion has emerged as a data management technique due to the diversity and scalability of data in health care. This first-scope review aimed to investigate the use of this technique in health care.

Materials and Methods: A query was carried out on PubMed, Science Direct, and EMBASE within 1960-2017 using such keywords as decision fusion, information fusion, symbolic fusion, distributed decisions, expert fusion, and sensor fusion, in conjunction with med-* and health-care. The articles were analyzed in terms of methodology and results.

Results: The literature search yielded 106 articles.  Based on the results, in the field of health care, the articles were related to image processing (29%), sensors (22%), diagnosis area(10%), biology (6%), health informatics (8%), and signal process (15%). The majority of articles were published in 2011, 2012, and 2015, and the USA had the largest number of articles. Most of the articles were about engineering and basic sciences. Regarding healthcare, the majority of studies were conducted on the diagnosis of diseases (80%), while 9% and 11% of articles were about prevention and treatment, respectively. These studies applied the following methods: intelligent methods (44%), new methods (36%), probabilistic (13%), and evidential methods (7%). The dataset was as follows: research project data (49%), online dataset (42%), and simulation (9%). Furthermore, 49% of articles mentioned the applied software, among which classification and interpretation were reportedly the most and the least used methods.

Discussion and Conclusion: Decision fusion is a holistic approach to evaluate all areas of health care and elucidate diverse techniques that can lead to improved quality of care.

Innovation: This article is the first scope review article about the application of the decision fusion technique in the field of health care, building on an established protocol. Decision fusion can reduce the cost of care and improve the quality of health care provision. Therefore, this article can help care providers understand the benefits of this technique and overcome challenges in adopting decision fusion technology.

https://doi.org/10.32592/ircmj.2020.22.10.30
PDF
HTML

References

  1. Nambiar R, Bhardwaj R, Sethi A, Vargheese R. A look at challenges and opportunities of big data analytics in healthcare. IEEE International Conference on Big Data, Silicon Valley, CA, USA; 2013. doi: 10.1109/BigData.2013.6691753.
  2. Zhang Q, Yang LT, Chen Z, Li P. A survey on deep learning for big data. Inf Fusion. 2018;42:146-57. doi: 10.1016/j.inffus. 2017.10.006.
  3. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2(1):3. doi: 10.1186/2047-2501-2-3. [PubMed: 25825667].
  4. Sagiroglu S, Sinanc D. Big Data: a review collaboration technologies and systems (CTS). 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA; 2013. doi: 10.1109/CTS.2013.6567202.
  5. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013;309(13):1351-2. doi: 10.1001/ jama.2013.393. [PubMed: 23549579].
  6. Martin AB, Hartman M, Washington B, Catlin A, National Health Expenditure Accounts Team. National health spending: faster growth in 2015 as coverage expands and utilization increases. Health Aff. 2016;36(1):166-76. doi: 10.1377/hlthaff.2016.1330. [PubMed: 27913569].
  7. Zhong H, Xiao J. Enhancing health risk prediction with deep learning on big data and revised fusion node paradigm. Sci Program. 2017;2017:1901876. doi: 10.1155/2017/1901876.
  8. Jagadish HV, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, et al. Big data and its technical challenges. Communic ACM. 2014;57(7):86-94. doi: 10.1145/2611567.
  9. Woźniak M, Graña M, Corchado E. A survey of multiple classifier systems as hybrid systems. Inf Fusion. 2014;16:3-17. doi: 10.1016/j.inffus.2013.04.006.
  10. Balazs JA, Velásquez JD. Opinion mining and information fusion: a survey. Inf Fusion. 2016;27:95-110. doi: 10.1016/ j.inffus.2015.06.002.
  11. Solaiman B, Debon R, Pipelier F, Cauvin JM, Roux C. Information fusion, application to data and model fusion for ultrasound image segmentation. IEEE Trans Biomed Eng. 1999;46(10):1171-5. doi: 10.1109/10.790491. [PubMed: 10513119].
  12. Torra V. Information fusion in data mining. New York: Association for Computing Machinery; 2009.
  13. Bossé É, Solaiman B. Information fusion and analytics for big data and IoT. Massachusetts: Artech House; 2016.
  14. Mangai UG, Samanta S, Das S, Chowdhury PR. A survey of decision fusion and feature fusion strategies for pattern classification. IETE Technical Rev. 2010;27(4):293-307. doi: 10.4103/0256-4602.64604.
  15. Lee D, Kim S, Kim Y. BioCAD: an information fusion platform for bio-network inference and analysis. BMC Bioinformatics. 2007;8(Suppl 9):S2. doi: 10.1186/1471-2105-8-S9-S2. [PubMed: 18047703].
  16. Synnergren J, Olsson B, Gamalielsson J. Classification of information fusion methods in systems biology. In Silico Biol. 2009;9(3):65-76. [PubMed: 19795566].
  17. Mirian MS, Ahmadabadi MN, Araabi BN, Siegwart RR. Learning active fusion of multiple experts' decisions: an attention-based approach. Neural Comput. 2011;23(2):558-91. doi: 10.1162/ NECO_a_00079. [PubMed: 21105824].
  18. Liu C, Zhao L, Tang H, Li Q, Wei S, Li J. Life-threatening false alarm rejection in ICU: using the rule-based and multi-channel information fusion method. Physiol Meas. 2016;37(8):1298-312. doi: 10.1088/0967-3334/37/8/1298. [PubMed: 27454710].
  19. Zein-Sabatto S, Mikhail M, Bodruzzaman M, DeSimio M, Derriso M, Behbahani A. Analysis of decision fusion algorithms in handling uncertainties for integrated health monitoring systems. Proc. SPIE 8407, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications; 2012. doi: 10.1117/12.919731.
  20. Song B, Li P. A novel decision fusion method based on weights of evidence model. Int J Image Data Fusion. 2014;5(2):123-37. doi: 10.1080/19479832.2014.894143.
  21. Velikova M, Lucas PJ, Samulski M, Karssemeijer N. A probabilistic framework for image information fusion with an application to mammographic analysis. Med Image Anal. 2012;16(4):865-75. doi: 10.1016/j.media.2012.01.003. [PubMed: 22326491].
  22. Lelandais B, Gardin I, Mouchard L, Vera P, Ruan S. Segmentation of biological target volumes on multi-tracer PET images based on information fusion for achieving dose painting in radiotherapy. Med Image Comput Comput Assist Interv. 2012;15(Pt 1):545-52. doi: 10.1007/978-3-642-33415-3_67. [PubMed: 23285594].
  23. Zheng M, Krishnan S, Tjoa MP. A fusion-based clinical decision support for disease diagnosis from endoscopic images. Comput Biol Med. 2005;35(3):259-74. doi: 10.1016/j.compbiomed. 2004.01.002. [PubMed: 15582632].
  24. Richard N, Dojat M, Garbay C. Automated segmentation of human brain MR images using a multi-agent approach. Artif Intell Med. 2004;30(2):153-76. doi: 10.1016/j.artmed.2003. 11.006. [PubMed: 15038368].
  25. Luo X, Wan Y, He X. Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion. Med Phys. 2015;42(4):1808-17. doi: 10.1118/1.4915285. [PubMed: 25832071].
  26. Barra V, Boire JY. Automatic segmentation of subcortical brain structures in MR images using information fusion. IEEE Trans Med Imaging. 2001;20(7):549-58. doi: 10.1109/42.932740. [PubMed: 11465462].
  27. Prasad S, Bruce LM, Ball JE. A multi-classifier and decision fusion framework for robust classification of mammographic masses. Annu Int Conf IEEE Med Biol Soc. 2008;2008:3048-51. doi: 10.1109/IEMBS.2008.4649846. [PubMed: 19163349].
  28. Isgum I, Staring M, Rutten A, Prokop M, Viergever MA, Van Ginneken B. Multi-atlas-based segmentation with local decision fusion—application to cardiac and aortic segmentation in CT scans. IEEE Trans Med Imaging. 2009;28(7):1000-10. doi: 10.1109/TMI.2008.2011480. [PubMed: 19131298].
  29. Niemeijer M, Abramoff MD, Van Ginneken B. Information fusion for diabetic retinopathy CAD in digital color fundus photographs. IEEE Trans Med Imaging. 2009;28(5):775-85. doi: 10.1109/TMI.2008.2012029. [PubMed: 19150786].
  30. Bosch M, Zhu F, Khanna N, Boushey CJ, Delp EJ. Combining global and local features for food identification in dietary assessment. Proc Int Conf Image Proc. 2011;2011:1789-92. doi: 10.1109/ICIP.2011.6115809. [PubMed: 25110454].
  31. Guo P, Banerjee K, Stanley RJ, Long R, Antani S, Thoma G, et al. Nuclei-based features for uterine cervical cancer histology image analysis with fusion-based classification. IEEE J Biomed Health Inform. 2016;20(6):1595-607. doi: 10.1109/JBHI.2015. 2483318. [PubMed: 26529792].
  32. Rahman MM, Bhattacharya P. An integrated and interactive decision support system for automated melanoma recognition of dermoscopic images. Comput Med Imaging Graph. 2010;34(6):479-86. doi: 10.1016/j.compmedimag.2009.10.003. [PubMed: 19942406].
  33. Kamali T, Boostani R, Parsaei H. A multi-classifier approach to MUAP classification for diagnosis of neuromuscular disorders. IEEE Trans Neural Syst Rehabil Eng. 2014;22(1):191-200. doi: 10.1109/TNSRE.2013.2291322. [PubMed: 24263096].
  34. Cai J, Lu L, Zhang Z, Xing F, Yang L, Yin Q. Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural networks. Med Image Comput Comput Assist Interv. 2016;9901:442-50. doi: 10.1007/978-3-319-46723-8_51. [PubMed: 28083570].
  35. Sert E, Ertekin S, Halici U. Ensemble of convolutional neural networks for classification of breast microcalcification from mammograms. Annu Int Conf IEEE Eng Med Biol Soc. 2017;2017:689-92. doi: 10.1109/EMBC.2017.8036918. [PubMed: 29059966].
  36. Depeursinge A, Racoceanu D, Iavindrasana J, Cohen G, Platon A, Poletti PA, et al. Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography. Artif Intell Med. 2010;50(1):13-21. doi: 10.1016/j.artmed. 2010.04.006. [PubMed: 20547044].
  37. Tiwari P, Viswanath S, Kurhanewicz J, Sridhar A, Madabhushi A. Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection. NMR Biomed. 2012;25(4):607-19. doi: 10.1002/nbm.1777. [PubMed: 21960175].
  38. Wei J, Chan HP, Zhou C, Wu YT, Sahiner B, Hadjiiski LM, et al. Computer‐aided detection of breast masses: four‐view strategy for screening mammography. Med Phys. 2011;38(4):1867-76. doi: 10.1118/1.3560462. [PubMed: 21626920].
  39. Zanaty E. An approach based on fusion concepts for improving brain magnetic resonance images (MRIs) segmentation. J Med Imaging Health Inf. 2013;3(1):30-7. doi: 10.1166/jmihi. 2013.1122.
  40. Zhu C, Jiang T. Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images. Neuroimage. 2003;18(3):685-96. doi: 10.1016/s1053-8119(03)00006-5. [PubMed: 12667846].
  41. Meng X, Zhang ZQ, Wu JK, Wong WC. Hierarchical information fusion for global displacement estimation in microsensor motion capture. IEEE Trans Biomed Eng. 2013;60(7):2052-63. doi: 10.1109/TBME.2013.2248085. [PubMed: 23446028].
  42. Lelandais B, Ruan S, Denœux T, Vera P, Gardin I. Fusion of multi-tracer PET images for dose painting. Med Image Anal. 2014;18(7):1247-59. doi: 10.1016/j.media.2014.06.014. [PubMed: 25128684].
  43. Ballanger B, Tremblay L, Sgambato-Faure V, Beaudoin-Gobert M, Lavenne F, Le Bars D, et al. A multi-atlas based method for automated anatomical Macaca fascicularis brain MRI segmentation and PET kinetic extraction. Neuroimage. 2013; 77:26-43. doi: 10.1016/j.neuroimage.2013.03.029. [PubMed: 23537938].
  44. Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage. 2006;33(1): 115-26. doi: 10.1016/j.neuroimage.2006.05.061. [PubMed: 16860573].
  45. Tahir BA, Swift AJ, Marshall H, Parra-Robles J, Hatton MQ, Hartley R, et al. A method for quantitative analysis of regional lung ventilation using deformable image registration of CT and hybrid hyperpolarized gas/1H MRI. Phys Med Biol. 2014;59(23):7267-77. doi: 10.1088/0031-9155/59/23/7267. [PubMed: 25383657].
  46. Mahdavi SS, Moradi M, Morris WJ, Goldenberg SL, Salcudean SE. Fusion of ultrasound B-mode and vibro-elastography images for automatic 3-D segmentation of the prostate. IEEE Trans Med Imaging. 2012;31(11):2073-82. doi: 10.1109/TMI.2012.2209204. [PubMed: 22829391].
  47. Yang K, Koo HW, Park W, Kim JS, Choi CG, Park JC, et al. Fusion 3-dimensional angiography of both internal carotid arteries in the evaluation of anterior communicating artery aneurysms. World Neurosurg. 2017;98:484-91. doi: 10.1016/j.wneu. 2016.11.047. [PubMed: 27876661].
  48. Akhondi-Asl A, Hoyte L, Lockhart ME, Warfield SK. A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights. IEEE Trans Med Imaging. 2014;33(10):1997-2009. doi: 10.1109/TMI.2014.2329603. [PubMed: 24951681].
  49. Kook H, Gupta L, Kota S, Molfese D. A dynamic multi-channel decision-fusion strategy to classify differential brain activity. Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:3212-5. doi: 10.1109/IEMBS.2007.4353013. [PubMed: 18002679].
  50. Antink CH, Gao H, Brüser C, Leonhardt S. Beat-to-beat heart rate estimation fusing multimodal video and sensor data. Biomed Opt Express. 2015;6(8):2895-907. doi: 10.1364/BOE.6.002895. [PubMed: 26309754].
  51. Poursaberi A, Noubari HA, Gavrilova M, Yanushkevich SN. Gauss–Laguerre wavelet textural feature fusion with geometrical information for facial expression identification. EURASIP J Image Video Proc. 2012;2012(1):17. doi: 10.1186/1687-5281-2012-17.
  52. Ren H, Kazanzides P. Hybrid attitude estimation for laparoscopic surgical tools: a preliminary study. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5583-6. doi: 10.1109/IEMBS.2009.5333487. [PubMed: 19964132].
  53. Ren H, Rank D, Merdes M, Stallkamp J, Kazanzides P. Development of a wireless hybrid navigation system for laparoscopic surgery. Stud Health Technol Inform. 2011; 163:479-85. [PubMed: 21335843].
  54. Tannous H, Istrate D, Benlarbi-Delai A, Sarrazin J, Gamet D, Ho Ba Tho MC, et al. A new multi-sensor fusion scheme to improve the accuracy of knee flexion kinematics for functional rehabilitation movements. Sensors. 2016;16(11):1914. doi: 10.3390/s16111914. [PubMed: 27854288].
  55. Xiong F, Hipszer BR, Joseph J, Kam M. Improved blood glucose estimation through multi-sensor fusion. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:377-80. doi: 10.1109/IEMBS. 2011.6090122. [PubMed: 22254327].
  56. Zhang Z, Luo X. Heartbeat classification using decision level fusion. Biomed Eng Lett. 2014;4(4):388-95. doi: 10.1007/ s13534-014-0158-7.
  57. Haase S, Forman C, Kilgus T, Bammer R, Maier-Hein L, Hornegger J. ToF/RGB sensor fusion for 3-D endoscopy. Curr Med Imaging. 2013;9(2):113-9.
  58. Liu S, Gao RX, John D, Staudenmayer J, Freedson PS. SVM-based multi-sensor fusion for free-living physical activity assessment. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:3188-91. doi: 10.1109/IEMBS.2011.6090868. [PubMed: 22255017].
  59. Chowdhury AK, Tjondronegoro D, Chandran V, Trost SG. Ensemble methods for classification of physical activities from wrist accelerometry. Med Sci Sports Exerc. 2017;49(9):1965-73. doi: 10.1249/MSS.0000000000001291. [PubMed: 28419025].
  60. Liu YT, Pal NR, Marathe AR, Wang YK, Lin CT. Fuzzy decision-making fuser (fdmf) for integrating human-machine autonomous (hma) systems with adaptive evidence sources. Front Neurosci. 2017;11:332. doi: 10.3389/fnins.2017.00332. [PubMed: 28676734].
  61. Qi J, Yang P, Hanneghan M, Tang S. Multiple density maps information fusion for effectively assessing intensity pattern of lifelogging physical activity. Neurocomputing. 2017;220:199-209. doi: 10.1016/j.neucom.2016.06.073.
  62. Sung WT, Chang KY. Evidence-based multi-sensor information fusion for remote health care systems. Sensors Actuators A Phys. 2013;204:1-19. doi: 10.1016/j.sna.2013.09.034.
  63. Neumuth T, Meissner C. Online recognition of surgical instruments by information fusion. Int J Comput Assist Radiol Surg. 2012;7(2):297-304. doi: 10.1007/s11548-011-0662-5. [PubMed: 22005841].
  64. Chowdhury A, Tjondronegoro D, Chandran V, Trost S. Physical activity recognition using posterior-adapted class-based fusion of multi-accelerometers data. IEEE J Biomed Health Inform. 2018;22(3):678-85. doi: 10.1109/JBHI.2017.2705036. [PubMed: 28534801].
  65. Anderson F, Birch DW, Boulanger P, Bischof WF. Sensor fusion for laparoscopic surgery skill acquisition. Computer Aided Surg. 2012;17(6):269-83. doi: 10.3109/10929088.2012.727641. [PubMed: 23098188].
  66. Chen C, Ugon A, Zhang X, Amara A, Garda P, Ganascia JG, et al. Personalized sleep staging system using evolutionary algorithm and symbolic fusion. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:2266-9. doi: 10.1109/EMBC.2016.7591181. [PubMed: 28268780].
  67. Yang P, Dumont GA, Ansermino JM. Sensor fusion using a hybrid median filter for artifact removal in intraoperative heart rate monitoring. J Clin Monit Comput. 2009;23(2):75-83. doi: 10.1007/s10877-009-9163-2. [PubMed: 19199059].
  68. Fontana JM, Farooq M, Sazonov E. Automatic ingestion monitor: a novel wearable device for monitoring of ingestive behavior. IEEE Trans Biomed Eng. 2014;61(6):1772-9. doi: 10.1109/TBME.2014.2306773. [PubMed: 24845288].
  69. Gupta L, Chung B, Srinath MD, Molfese DL, Kook H. Multichannel fusion models for the parametric classification of differential brain activity. IEEE Trans Biomed Eng. 2005; 52(11):1869-81. doi: 10.1109/TBME.2005.856272. [PubMed: 16285391].
  70. Köhler T, Haase S, Bauer S, Wasza J, Kilgus T, Maier-Hein L, et al. Multi-sensor super-resolution for hybrid range imaging with application to 3-D endoscopy and open surgery. Med Image Anal. 2015;24(1):220-34. doi: 10.1016/j.media. 2015.06.011. [PubMed: 26201876].
  71. Yue D, Guo M, Chen Y, Huang Y. A Bayesian decision fusion approach for microRNA target prediction. BMC Genomics. 2012;13(Suppl 8):S13. doi: 10.1186/1471-2164-13-S8-S13. [PubMed: 23282032].
  72. Liu F, Zhang SW, Guo WF, Wei ZG, Chen L. Inference of gene regulatory network based on local bayesian networks. PLoS Computat Biol. 2016;12(8):e1005024. doi: 10.1371/journal. pcbi.1005024. [PubMed: 27479082].
  73. Chen J, Xu H, He PA, Dai Q, Yao Y. A multiple information fusion method for predicting subcellular locations of two different types of bacterial protein simultaneously. Biosystems. 2016;139:37-45. doi: 10.1016/j.biosystems.2015.12.002. [PubMed: 26724384].
  74. Kasturi J, Acharya R. Clustering of diverse genomic data using information fusion. Bioinformatics. 2004;21(4):423-9. doi: 10.1093/bioinformatics/bti186. [PubMed: 15608052].
  75. Liu H, Shi X, Guo D, Zhao Z. Feature selection combined with neural network structure optimization for HIV-1 protease cleavage site prediction. Biomed Res Int. 2015;2015:263586. doi: 10.1155/2015/263586. [PubMed: 25961009].
  76. Chen Y, Xu J, Yang B, Zhao Y, He W. A novel method for prediction of protein interaction sites based on integrated RBF neural networks. Comput Biol Med. 2012;42(4):402-7. doi: 10.1016/j.compbiomed.2011.12.007. [PubMed: 22226645].
  77. Re M, Valentini G. Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machines. Neurocomputing. 2010;73(7-9):1533-7. doi: 10.1016/j.neucom.2009.12.012.
  78. Zhang SW, Hao LY, Zhang TH. Prediction of protein–protein interaction with pairwise kernel support vector machine. Int J Mol Sci. 2014;15(2):3220-33. doi: 10.3390/ijms15023220. [PubMed: 24566145].
  79. Zhang YC, Zhang SW, Liu L, Liu H, Zhang L, Cui X, et al. Spatially enhanced differential RNA methylation analysis from affinity-Based sequencing data with hidden markov model. Biomed Res Int. 2015;2015:852070. doi: 10.1155/2015/852070. [PubMed: 26301253].
  80. Zhang S, Han J, Liu J, Zheng J, Liu R. An improved poly (A) motifs recognition method based on decision level fusion. Comput Biol Chem. 2015;54:49-56. doi: 10.1016/j.com pbiolchem.2014.12.001. [PubMed: 25594576].
  81. Singh R, Murad W. Protein disulfide topology determination through the fusion of mass spectrometric analysis and sequence-based prediction using Dempster-Shafer theory. BMC Bioinformatics. 2013;2(Suppl 2):S20. doi: 10.1186/1471-2105-14-S2-S20. [PubMed: 23368815].
  82. Kolesar I, Parulek J, Viola I, Bruckner S, Stavrum AK, Hauser H. Interactively illustrating polymerization using three-level model fusion. BMC Bioinformatics. 2014;15(1):345. doi: 10.1186/1471-2105-15-345. [PubMed: 25315282].
  83. Zhang SW, Zhang TH, Zhang JN, Huang Y. Prediction of signal peptide cleavage sites with subsite‐coupled and template matching fusion algorithm. Mol Inform. 2014;33(3):230-9. doi: 10.1002/minf.201300077. [PubMed: 27485691].
  84. Chua HN, Sung WK, Wong L. An efficient strategy for extensive integration of diverse biological data for protein function prediction. Bioinformatics. 2007;23(24):3364-73. doi: . 10.1093/bioinformatics/btm520. [PubMed: 18048396].
  85. Kirshin E, Oreshkin B, Zhu GK, Popovic M, Coates M. Microwave radar and microwave-induced thermoacoustics: Dual-modality approach for breast cancer detection. IEEE Trans Biomed Eng. 2013;60(2):354-60. doi: 10.1109/TBME. 2012.2220768. [PubMed: 23193227].
  86. Daunizeau J, Grova C, Marrelec G, Mattout J, Jbabdi S, Pélégrini-Issac M, et al. Symmetrical event-related EEG/fMRI information fusion in a variational Bayesian framework. Neuroimage. 2007;36(1):69-87. doi: 10.1016/j.neuroimage. 2007.01.044. [PubMed: 17408972].
  87. Moslem B, Diab M, Marque C, Khalil M. Classification of multichannel uterine EMG signals. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:2602-5. doi: 10.1109/IEMBS.2011. 6090718. [PubMed: 22254874].
  88. Kochi N, Helikar T, Allen L, Rogers JA, Wang Z, Matache MT. Sensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functions. BMC Syst Biol. 2014;8(1):92. doi: 10.1186/s12918-014-0092-4. [PubMed: 25189194].
  89. Fan Y, Yin Y. Active and progressive exoskeleton rehabilitation using multisource information fusion from emg and force-position EPP. IEEE Trans Biomed Eng. 2013;60(12):3314-21. doi: 10.1109/TBME.2013.2267741. [PubMed: 23771306].
  90. Santana R, Bielza C, Larrañaga P. Regularized logistic regression and multiobjective variable selection for classifying MEG data. Biol Cybern. 2012;106(6-7):389-405. doi: 10.1007/s00422-012-0506-6. [PubMed: 22854976].
  91. Qian M, Aguilar M, Zachery KN, Privitera C, Klein S, Carney T, et al. Decision-level fusion of EEG and pupil features for single-trial visual detection analysis. IEEE Trans Biomed Eng. 2009;56(7):1929-37. doi: 10.1109/TBME.2009.2016670. [PubMed: 19336285].
  92. Liang F, Xie W, Yu Y. Beating heart motion accurate prediction method based on interactive multiple model: an information fusion approach. Biomed Res Int. 2017;2017:1279486. doi: 10.1155/2017/1279486. [PubMed: 29124062].
  93. Malarvili M, Mesbah M. Combining newborn EEG and HRV information for automatic seizure detection. Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:4756-9. doi: 10.1109/IEMBS.2008.4650276. [PubMed: 19163779].
  94. O’Regan S, Marnane W. Multimodal detection of head-movement artefacts in EEG. J Neurosci Methods. 2013; 218(1):110-20. doi: 10.1016/j.jneumeth.2013.04.017. [PubMed: 23685269].
  95. Chowdhury RA, Zerouali Y, Hedrich T, Heers M, Kobayashi E, Lina JM, et al. MEG–EEG information fusion and electromagnetic source imaging: from theory to clinical application in epilepsy. Brain Topogr. 2015;28(6):785-812. doi: 10.1007/s10548-015-0437-3. [PubMed: 26016950].
  96. Antink CH, Leonhardt S, Walter M. A synthesizer framework for multimodal cardiorespiratory signals. Biomed Phys Eng Expr. 2017;3(3):035028.
  97. Acharya S, Rajasekar A, Shender BS, Hrebien L, Kam M. Real-Time hypoxia prediction using decision fusion. IEEE J Biomed Health Inform. 2017;21(3):696-707. doi: 10.1109/JBHI.2016. 2528887. [PubMed: 26887018].
  98. Jesneck JL, Nolte LW, Baker JA, Floyd CE, Lo JY. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis. Med Phys. 2006;33(8):2945-54. doi: 10.1118/1.2208934. [PubMed: 16964873].
  99. Li GZ, Yan SX, You M, Sun S, Ou A. Intelligent ZHENG classification of hypertension depending on ML-kNN and information fusion. Evid Based Complement Alternat Med. 2012;2012:837245. doi: 10.1155/2012/837245. [PubMed: 22701510].
  100. Wang YQ, Yan HX, Guo R, Li FF, Xia CM, Yan JJ, et al. Study on intelligent syndrome differentiation in Traditional Chinese Medicine based on multiple information fusion methods. Int J Data Min Bioinform. 2011;5(4):369-82. doi: 10.1504/ijdmb.2011.041554. [PubMed: 21954670].
  101. Ahiskali M, Green D, Kounios J, Clark CM, Polikar R. ERP based decision fusion for AD diagnosis across cohorts. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2494-7. doi: 10.1109/IEMBS.2009.5335141. [PubMed: 19965206].
  102. Stroud J, Enverga I, Silverstein T, Song B, Rogers T. Ensemble learning and the heritage health prize. California: University of California; 2012.
  103. Wang J, Hu Y, Xiao F, Deng X, Deng Y. A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster–Shafer theory of evidence: an application in medical diagnosis. Artif Intell Med. 2016;69:1-11. doi: 10.1016/j.artmed.2016.04.004. [PubMed: 27235800].
  104. Xuming Y, Yijun Y, Yong X, Xuanzhong W, Zheyu W, Hongmei N, et al. A precise and accurate acupoint location obtained on the face using consistency matrix pointwise fusion method. J Tradit Chin Med. 2015;35(1):110-6. doi: 10.1016/s0254-6272(15)30017-0. [PubMed: 25842737].
  105. Li S, Liu G, Tang X, Lu J, Hu J. An ensemble deep convolutional neural network model with improved DS evidence fusion for bearing fault diagnosis. Sensors. 2017;17(8):1729. doi: 10.3390/s17081729. [PubMed: 28788099].
  106. Ooi KEB, Lech M, Allen NB. Multichannel weighted speech classification system for prediction of major depression in adolescents. IEEE Trans Biomed Eng. 2013;60(2):497-506. doi: 10.1109/TBME.2012.2228646. [PubMed: 23192475].
  107. Mou Q, Xu Z, Liao H. An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Inf Sci. 2016;374:224-39. doi: 10.1016/j.ins.2016.08.074.
  108. Mnatsakanyan ZR, Burkom HS, Hashemian MR, Coletta MA. Distributed information fusion models for regional public health surveillance. Inf Fusion. 2012;13(2):129-36. doi: 10.1016/j.inffus.2010.12.002.
  109. Yang P, Xu L, Zhou BB, Zhang Z, Zomaya AY. A particle swarm based hybrid system for imbalanced medical data sampling. BMC Genomics. 2009;10(Suppl 3):S34. doi: 10.1186/1471-2164-10-S3-S34. [PubMed: 19958499].
  110. Mei J, Liu H, Li X, Xie GT, Yu Y. A decision fusion framework for treatment recommendation systems. Stud Health Technol Inform. 2015;216:300-4. [PubMed: 26262059].
  111. Quellec G, Lamard M, Cazuguel G, Roux C, Cochener B. Case retrieval in medical databases by fusing heterogeneous information. IEEE Trans Med Imaging. 2011;30(1):108-18. doi: 10.1109/TMI.2010.2063711. [PubMed: 20693107].
  112. Lecornu L, Le Guillou C, Le Saux F, Hubert M, Puentes J, Montagner J, et al. Information fusion for diagnosis coding support. Annu Int Conf IEEE Eng Med Biol Soc. 2011; 2011:3176-9. doi: 10.1109/IEMBS.2011.6090865. [PubMed: 22255014].
  113. Sokolova MV, Fernández-Caballero A. Modeling and implementing an agent-based environmental health impact decision support system. Expert Syst Appl. 2009;36(2):2603-14. doi: 10.1016/j.eswa.2008.01.041.
  114. Mirian MS, Ahmadabadi MN, Araabi BN, Siegwart RR. Learning active fusion of multiple experts' decisions: an attention-based approach. Neural Comput. 2011;23(2):558-91. doi: 10.1162/NECO_a_00079. [PubMed: 21105824].
  115. Chen J, Yu H. Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients. J Biomed Inform. 2017;68:121-31. doi: 10.1016/j.jbi.2017.02.016. [PubMed: 28267590].
  116. Comaniciu D, Zhou XS, Krishnan S. Robust real-time myocardial border tracking for echocardiography: an information fusion approach. IEEE Trans Med Imaging. 2004;23(7):849-60. doi: 10.1109/TMI.2004.827967. [PubMed: 15250637].
  117. Velikova M, Lucas PJ, Samulski M, Karssemeijer N. A probabilistic framework for image information fusion with an application to mammographic analysis. Med Image Anal. 2012;16(4):865-75. doi: 10.1016/j.media.2012.01.003. [PubMed: 22326491].
  118. Houcque D. Introduction to Matlab for engineering students. Evanston, Illinois: Northwestern University; 2005. P. 1-64.
  119. Ramírez-Gallego S, Fernández A, García S, Chen M, Herrera F. Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce. Inf Fusion. 2018;42:51-61. doi: 10.1016/j.inffus.2017.10.001.
  120. Ferranti A, Marcelloni F, Segatori A, Antonelli M, Ducange P. A distributed approach to multi-objective evolutionary generation of fuzzy rule-based classifiers from big data. Inf Sci. 2017;415:319-40. doi: 10.1016/j.ins.2017.06.039.
  121. Nazari E, Shahriari MH, Tabesh H. BigData analysis in healthcare: apache hadoop, apache spark and apache flink. Frontiers Health Inf. 2019;8(1):14. doi: 10.30699/fhi.v8i1.180.
  122. Al-Rfou R, Alain G, Almahairi A, Angermueller C, Bahdanau D, Ballas N, et al. Theano: a python framework for fast computation of mathematical expressions. New York: Eprint ArXiv; 2016.
  123. Nazari E, Pour R, Tabesh H. Comprehensive overview of decision-fusion technique in healthcare: a scoping review protocol. Frontiers Health Inf. 2018;7(1):e7. doi: 10.24200/ijmi.v7i0.164.