An Efficient Nearly-Optimum Decision Fusion Technique Based on Message Passing

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.

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A new decision fusion technique for image classification

2009 16th IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip.2009.5413846 ◽

2009 ◽

Cited By ~ 8

Author(s):

Mete Ozay ◽

Fatos Tunay ◽

Yarman Vural

Keyword(s):

Image Classification ◽

Decision Fusion ◽

Fusion Technique

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A message passing approach for decision fusion in adversarial multi-sensor networks

Information Fusion ◽

10.1016/j.inffus.2017.06.006 ◽

2018 ◽

Vol 40 ◽

pp. 101-111 ◽

Cited By ~ 1

Author(s):

Andrea Abrardo ◽

Mauro Barni ◽

Kassem Kallas ◽

Benedetta Tondi

Keyword(s):

Sensor Networks ◽

Message Passing ◽

Decision Fusion

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AN IMPROVED DECISION FUSION TECHNIQUE TO INCREASE THE PERFORMANCE LEVEL OF HRR ATR SYSTEMS

Progress In Electromagnetics Research ◽

10.2528/pier13031103 ◽

2013 ◽

Vol 139 ◽

pp. 87-104 ◽

Cited By ~ 1

Author(s):

Iulian Constantin Vizitiu

Keyword(s):

Decision Fusion ◽

Performance Level ◽

Fusion Technique

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Advantages and Challenges of Information Fusion Technique for Big Data Analysis: Proposed Framework

Journal of Biostatistics and Epidemiology ◽

10.18502/jbe.v7i2.6737 ◽

2021 ◽

Author(s):

Elham Nazari ◽

Rizwana Biviji ◽

Amir Hossein Farzin ◽

Parnian Asgari ◽

Hamed Tabesh

Keyword(s):

Data Analysis ◽

Information Fusion ◽

Decision Fusion ◽

Design Stage ◽

Process Data ◽

Fusion Technique ◽

Correct Usage ◽

Evaluation Stage ◽

Information Fusion Technique ◽

The Impact

Introduction: Recently, with the surge in the availability of relevant data in various industries, the use of Information Fusion technique for data analysis is increasing. This method has several advantages, such as increased accuracy, and the use of meaningful information. In addition, there are certain challenges, including the impact of data type and analytical method on results. The goal of this study is to propose a framework for introducing the advantages and classifying the challenges of this technique. Method: We conducted a review of articles published between January 1960 and December 2017 for the design stage and from January 2018 to December 2018 for the evaluation stage. Articles were identified from various databases such as Science Direct, IEEE, Scopus, Web of Science, and Google Scholar, using the keywords decision fusion, information fusion, and symbolic fusion. We report the advantages and challenges of the methodologies described in these articles. Analysis was conducted in accordance with PRISMA guidelines. Results: A total of 132 articles were identified in the design stage and 90 articles were identified in the evaluation stage. Categories within the framework for challenges include “hardware and software requirements for processing and maintaining the process”, “data” and “data analysis method”. The categories for advantages include “value modeling”, “preferable management of uncertainty and variability”, “excellent decision making”, “comprehensive interpretation and representation”, “data management” and “simplicity of infrastructure”. Our results indicate using these two frameworks with 95% Confidence interval. Conclusion: An overall understanding of the advantages and challenges of the information fusion technique could act as a guide for the researcher for the correct usage of this technique.

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