Application of artificial neural network method in medical health field: bibliometric analysis

Yan Xu; Shuaikang Miao

doi:10.47297/wspbdsWSP2752-630501.20240402

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Application of artificial neural network method in medical health field: bibliometric analysis

Volume 4, Issue 2

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Application of artificial neural network method in medical health field: bibliometric analysis
Application of artificial neural network method in medical health field: bibliometric analysis
Yunnan University of Finance and Economics, School of Logistics and Management Engineering, Kunming, Yunnan 650221, China
Vol. 4, Issue 2, Pages: 5-18(2024)
Published： 27 June 2024 ，
DOI：10.47297/wspbdsWSP2752-630501.20240402
Accepted：

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Xu Yan,Miao Shuaikang.Application of artificial neural network method in medical health field: bibliometric analysis[J].Biomedical Data Science,2024,04(02):5-18.
DOI：

Xu Yan,Miao Shuaikang.Application of artificial neural network method in medical health field: bibliometric analysis[J].Biomedical Data Science,2024,04(02):5-18. DOI： 10.47297/wspbdsWSP2752-630501.20240402.

摘要

Abstract

With the rapid development of artificial intelligence technology

artificial neural network (ANN) has been widely used in medical and health fields. This paper aims to systematically review and analyze the research status

hot spots and development trends of artificial neural networks in the field of medical health through bibliometric analysis. Firstly

we selected 3767 relevant literatures from the Web of Science database and used CiteSpace for data processing and analysis. The results show that in recent years

the research on artificial neural networks in disease diagnosis

personalized medicine

medical image analysis and drug discovery continues to increase

and shows a trend of interdisciplinary and international cooperation. Through keyword co-occurrence analysis

we identified current research hotspots

including deep learning

image processing

tumor detection

and genomics analysis. The analysis of highly cited literature further reveals the influential research results and key technologies. This paper summarizes the main application directions of artificial neural network in the field of medical health

and prospects the future development trend

in order to provide reference for related research.

关键词

Keywords

Medical and healthresearch reviewCiteSpacevisualization

references

Bai R Q, Zhao J M, Li D G, et al. RNN-based demand awareness in smart library using CRFID[J]. China Communications, 2020, 17(5):284-294.

Cao R, Guo K H, Ma J H, et al. A deep convolutional neural network- based label completion and correction strategy for supervised medical image learning[C]. Proceedings of IEEE SmartWorld, Guangzhou. New York: IEEE, 2018:1725-1730.

Deng L, Chu H H, Shi P, et al. Region-based CNN method with deformable modules for visually classifying concrete cracks[J]. Applied Sciences-Basel, 2020, 10(7):1-18.

Garcia-pedrajas N, Ortiz-boyer D, Hervasmartinez C. Cooperative coevolution of generalized multi-layer perceptrons[J]. Neurocomputing, 2004, 56:257- 283.

Hebb D O. The organization of behavior:a neuropsychological theory[M]. New Jersey:Lawrence Erlbaum Associates, 1949.

Hopfield J J. Neurons with graded response have collective computational properties like those of two- state neurons[J]. Proc Natl Acad Sci, 1984, 81(10):3088-3092.

Hu Q H, Souza L F D, Holanda G B, et al. An effective approach for CT lung segmentation using mask region- based convolutional neural networks[J]. Artificial Intelligence in Medicine, 2020, 103:1-36.

Hyojin J, Soosun C. Brief paper:drivable area detection with region-based CNN models to support autonomous driving[J]. Journal of Multimedia Information System, 2020, 7(1):41-44.

Ikuta C, Uwate Y, Nishio Y. Multi-layer perceptron with glial network for solving two-spiral problem[J]. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2011, E94A(9): 1864-1867.

Jin X, Liu Q, Long H Z. Impact of cost-benefit analysis on financial benefit evaluation of investment projects under back propagation neural network[J]. Journal of Computational and Applied Mathematics, 2021, 384:1-15.

Li Y P, Cao W M. An extended multilayer perceptron model using reduced geometric algebra[J]. IEEE Access, 2019, 7:129815-129823.

Ling J, Liu G J, Li J L, et al. Fault prediction method for nuclear power machinery based on Bayesian PPCA recurrent neural network model[J]. Nuclear Science and Techniques, 2020, 31(8):1-11.

Liu H, Tian H Q, Li Y F, et al. Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions[J]. Energy Conversion and Management, 2015, 92:67-81.

Lou G X, Shi H Z. Face image recognition based on convolutional neural network[J]. China Communications, 2020, 17(2):117-124.

Martinez-morales J, Quej-cosgaya H, Lagunasjimenez J, et al. Design optimization of multilayer perceptron neural network by ant colony optimization applied to engine emissions data[J]. Science China Technological Sciences, 2019, 62(6):1055-1064.

Masulli F, Penna M. Improving learning speed in multilayer perceptrons through principal component analysis[J]. Proceedings of the SPIE, 1996, 2760:85-95.

Mcculloch W S, Pitts W. A logical calculus of the ideas immanent in nervous activity[J]. Bulletin of Mathematical Biophysics, 1943, 5:115-133.

Miao Z N, Zheng H J. An improved back-propagation algorithm for fuzzy modeling[C]. Proceedings of the 3rd International Conference on Measuring Technology and Mechatronics Automation, Shanghai. Switzerland:Trans Tech Publications, 2011:198-202.

Mosavi A, Samadianfard S. Predicting soil electrical conductivity using multi-layer perceptron integrated with grey wolf optimizer[J]. Journal of Geochemical Exploration, 2021, 220:1-10.

Ng S C, Leung S H, Luk A. Fast convergent generalized back-propagation algorithm with constant learning rate[J]. Neural Processing Letters, 1999, 9:13-23.

Rosenblatt F. The perceptron:probabilistic model for information storage and organization in the brain[J]. Psychological Review, 1958, 65(6):386-408.

Rumelhart D E, HINTON G, Williams R J. Learning representations by back-propagating errors[J]. Nature,1986, 323(6088):533-536.

Sang B. Application of genetic algorithm and BP neural network in supply chain finance under information sharing[J]. Journal of Computational and Applied Mathematics, 2021, 384:1-11.

Silaban H, Zarlis M, Sawaluddin. Analysis of accuracy and epoch on back-propagation BFGS quasiNewton[C]. Proceedings of International Conference on Information and Communication Technology,Medan, 2017.

Stender M, Tiedemann M, Spieler D, et al. Deep learning for brake squeal:brake noise detection, characterization and prediction[J]. Mechanical Systems and Signal Processing, 2021, 149:1-27.

Sutton R T, Pincock D, Baumgart D C, et al. An overview of clinical decision support systems: benefits, risks, and strategies for success[J]. NPJ digital medicine, 2020, 3(1): 17.

Wei X, Zhang L L, Yang H Q, et al. Machine learning for pore-water pressure time- series prediction:application of recurrent neural networks[J]. Geoscience Frontiers, 2021, 12(1):453-467.

Zhang J Q, Sun Y, Guo L, et al. A new bearing fault diagnosis method based on modified convolutional neural networks[J]. Chinese Journal of Aeronautics, 2020, 33(2):439-447.

Zhu Q, Li J. Applied technology on artificial neural network in fault diagnosis system[C]. Proceedings of the 2nd International Conference on Civil Engineering and Material Engineering, Wuhan.Switzerland:Trans Tech Publications, 2014:448-452.

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