Decision fusion in healthcare and medicine: a narrative review

Abstract

To provide an overview of the Decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels. Background: The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information.Methods: We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion, information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review.Conclusions: Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector.

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

“…There are several models that can be ensembled with CNN designs which can be used for medical image analyses. Ensemble techniques aim to improve the robustness and accuracy of CNNs [75][76][77]. Ensemble methods for CNN models include mixture ensemble of CNNs [78] used for breast tumor classification [79], ensembles of pre-trained CNNs (such as inception v3) [80] used for epilepsy classification [78], in-network ensembles for obstructive sleep apnea detection [81,82], weighted average ensembles for pneumonia detection [83], a self-ensemble framework [84] used for brain lesion segmentation, orthogonal and attentive ensemble networks [85] used for COVID-19 diagnosis [86,87], 3D CNN ensembles used for pulmonary nodule classification in lung cancer screening [88] and ensembles of REFINED-CNN built under different choices of distance metrics and/or projection schemes used for anti-cancer drug sensitivity prediction [89].…”

Identification of BCL11A, NTN5, and OGN as diagnosis biomarker of papillary renal cell carcinomas by bioinformatic analysis

“…Therefore, machine learning methods, which are a sub eld of arti cial intelligence that provides computers with the ability to learn without having to be explicitly programmed, have become an increasingly popular tool for medical researchers. By applying these techniques, patterns and relationships can be discovered and identi ed from complex datasets, while they are capable of predicting future outcomes of a given type of cancer (10)(11)(12)(13)(14). As a result, these techniques have become increasingly popular and various biomarkers have been identi ed for the diagnosis, prognosis, and treatment of a wide range of cancers, including breast cancer, prostate cancer, pancreatic cancer, and colorectal cancer in recent years (15)(16)(17)(18).…”

Identification of ZMYND19 as a novel biomarker of colorectal cancer: RNA-sequencing and machine learning analysis

“…Bioinformatic technology such as next-generation sequencing and microarray analysis has provided a significant source of mutational and transcriptional variations of different cancers. A new branch of artificial intelligence is machine learning techniques is a promising approach in identifying novel biomarkers for early diagnosis and prognosis and also providing effective treatment (Nazari et al 2020(Nazari et al , 2021(Nazari et al , 2022. These new approaches enable researchers to identify dysregulated pathways and novel biomarkers (Kelley and Venook 2011).…”