Decision fusion in healthcare and medicine : a narrative review

Objective: 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 (DF), 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

The Cognitive and Neural Basis for Apathy in Frontotemporal Degeneration

The syndrome of apathy, defined as a reduction in goal-directed behavior (GDB), has profound consequences for morbidity and mortality in the patient and for family-caregiver burden. Apathy is one of the primary neuropsychiatric syndromes associated with the disruption of the frontal-striatal system, but the behavioral and biological mechanisms underlying apathy are not well understood. Apathy is especially prevalent in behavioral variant frontotemporal degeneration (bvFTD). In a sample of 20 apathetic adults with bvFTD and 17 normal controls (NC), impairments in three components of GDB--initiation, planning and motivation--were examined using a novel computerized reaction time test. Employing structural neuroimaging techniques, I then examined the neural basis of GDB in these apathetic bvFTD participants. I found evidence that apathy is associated with an impairment in any of the three GDB components. Initiation, planning, and motivation each map onto three distinct brain regions in the frontal lobe that work together in a large-scale neural network. Furthermore, I was able to identify participants with specific subtypes of apathy, depending on the impaired GDB mechanism. I developed and submitted a proposal for continued study of the phenomenon; the proposal was awarded. The long-term potential impact of this beginning program of research is profound for patients with neurodegenerative disease, their caregivers, and families. Current treatment of apathy has been hindered due to poor understanding of the mechanisms underlying this condition. This work will lead to a better understanding of these mechanisms and structures fundamental to the behavior, and, with this knowledge, tailored interventions can be designed and implemented by professional and lay caregivers. Thus, a more precise characterization of apathy will allow providers to implement the most appropriate therapy for a given patient