Multi-source information fusion based on rough set theory: A review

Zhang, Pengfei; Li, Tianrui; Wang, Guoqiang; Luo, Chuan; Chen, Hongmei; Zhang, Junbo; Wang, De‐Xian; Zeng, Yu

doi:10.1016/j.inffus.2020.11.004

Cited by 194 publications

(40 citation statements)

References 243 publications

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“…The formal concepts are clusters that indicate relationships hidden in the data among patients with a common subset of lifestyle factors. Concepts are derived from the table containing patient factors called 'context' [26]. By ordering concepts, a mathematical structure called a concept lattice is obtained that describes relationships between individual groups (concepts) of patients with a shared set of factors.…”

Section: Analytical Model: Grouping Patients Into Overlapping Clustersmentioning

confidence: 99%

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

et al. 2021

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Background The burden of chronic and societal diseases is affected by many risk factors that can change over time. The minimalisation of disease-associated risk factors may contribute to long-term health. Therefore, new data-driven health management should be used in clinical decision-making in order to minimise future individual risks of disease and adverse health effects. Methods We aimed to develop a health trajectories (HT) management methodology based on electronic health records (EHR) and analysing overlapping groups of patients who share a similar risk of developing a particular disease or experiencing specific adverse health effects. Formal concept analysis (FCA) was applied to identify and visualise overlapping patient groups, as well as for decision-making. To demonstrate its capabilities, the theoretical model presented uses genuine data from a local total knee arthroplasty (TKA) register (a total of 1885 patients) and shows the influence of step by step changes in five lifestyle factors (BMI, smoking, activity, sports and long-distance walking) on the risk of early reoperation after TKA. Results The theoretical model of HT management demonstrates the potential of using EHR data to make data-driven recommendations to support both patients’ and physicians’ decision-making. The model example developed from the TKA register acts as a clinical decision-making tool, built to show surgeons and patients the likelihood of early reoperation after TKA and how the likelihood changes when factors are modified. The presented data-driven tool suits an individualised approach to health management because it quantifies the impact of various combinations of factors on the early reoperation rate after TKA and shows alternative combinations of factors that may change the reoperation risk. Conclusion This theoretical model introduces future HT management as an understandable way of conceiving patients’ futures with a view to positively (or negatively) changing their behaviour. The model’s ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.

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Section: Analytical Model: Grouping Patients Into Overlapping Clustersmentioning

confidence: 99%

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

et al. 2021

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“…At that time, the information fusion technology was considered to start from multiple sensor data and related information sources, combined with the evaluation process, to achieve the positioning of the target and the comprehensive evaluation of the threat and importance of the battlefield. The basic principle of information fusion is by the biological brain processing a variety of information streams, combined with computer technology, the information acquisition system for regular processing [15][16][17]. Calculate, analyse, and preprocess to get the status information of the target.…”

Section: Information Fusion Technologymentioning

confidence: 99%

Optimization of Historic Building Survey Technology under Artificial Intelligence Wireless Network Technology Environment

Tan

Fen

2021

Wireless Communications and Mobile Computing

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In order to optimize the technology of the building, the damage identification of the building structure is studied. Firstly, back propagation neural network (BPNN) and information fusion technology are used to build neural network models. Secondly, the established model is trained. Finally, the displacement mode, natural frequency, Modal Assurance Criterion (MAC), and three kinds of information fusion with only one characteristic information are used as input data to analyse the results of BPNN identification damage. The results show that when the natural frequency is used as the sensitive feature of damage, the accuracy is the highest. The difference between the network output value and the expected value is the smallest, the network output is the most stable, and the network recognition effect is the best. The network output of a mixture of two damage depths is compared with the output of a single damage depth. The data of the network training set composed of the feature data with damage depth of 20 mm and 5 mm has higher accuracy and more accurate damage recognition. This research provides a reference for the optimization of building survey technology and has certain practical value.

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“…However, it is still an open issue that how to effectively combine the uncertain, inconsistent, or even conflicted data collected from different sensors. Many theories have been presented to solve this problem, including Dempster–Shafer (D–S) evidence theory, 1,2 fuzzy set theory, 3,4 rough sets, 5,6 Z -numbers, 7,8 D -numbers, 9,10 R -number, 11,12 and so on.…”

Section: Introductionmentioning

confidence: 99%

A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion

Hou

Deng

et al. 2021

International Journal of Distributed Sensor Networks

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Dempster–Shafer (D–S) evidence theory is more and more extensively applied in multi-sensor data fusion. However, it is still an open issue that how to effectively combine highly conflicting evidence in D–S evidence theory. In this article, a novel divergence measure, called pignistic probability transformation divergence, is proposed to measure the difference between evidences. The proposed pignistic probability transformation divergence can reflect the interaction between single-element and multi-element subsets by introducing the pignistic probability transformation, and satisfies the properties of boundedness, non-degeneracy, and symmetry. Moreover, the pignistic probability transformation divergence can degenerate as Jensen–Shannon divergence when mass function and the probability distribution are consistent. Based on the pignistic probability transformation divergence, a new multi-sensor data fusion method is presented. The proposed method takes advantage of pignistic probability transformation divergence to measure the discrepancy between evidences in order to obtain the credibility weights, and belief entropy to measure the uncertainty of the evidences in order to obtain the information volume weights, which can fully mine the potential information between evidences. Then, the credibility weights and the information volume weights are integrated to generate an appropriate weighted average evidence before using Dempster’s combination rule. The results of two application cases illustrate that the proposed method outperforms other related methods for combining highly conflicting evidences.

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Multi-source information fusion based on rough set theory: A review

Cited by 194 publications

References 243 publications

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

Optimization of Historic Building Survey Technology under Artificial Intelligence Wireless Network Technology Environment

A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion

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