A fuzzy cognitive map hierarchical model for differential diagnosis of dysarthrias and apraxia of speech

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.

Preliminary evaluation of a personal healthcare system prototype for cognitive eRehabilitation in a living assistance domain

The integration of rehabilitation systems in an ambient assisted living environment can provide a powerful and versatile tool for long-term stroke rehabilitation goals. This paper introduces a novel concept of a personalized cognitive rehabilitation system in a naturalistic setting. The proposed platform was developed within the CogWatch project, with the intent of fostering independence in activities of daily living in patients with apraxia and action disorganization syndrome. Technical usability was evaluated in a series of pilot experiments, which illustrate how this approach may help to retrain patients in activities of daily living. The first system prototype has been tested with 36 participants divided into three groups, providing an exploratory evaluation of the usability of this solution and its acceptability. The technical solutions used within the CogWatch project are targeted to meet both the end users' needs from the interaction and usability point of views and the clinical requirements associated with the use of such systems. The challenges behind the development of ambient assisted living systems for cognitive rehabilitation are discussed.

A systems perspective on the effective connectivity of overt speech production

The aim of this study was to provide a computational system model of effective connectivity in the human brain underlying overt speech production. Meta-analysis of neuroimaging studies and functional magnetic resonance imaging data acquired during a verbal fluency task revealed a core network consisting of Brodmann's area (BA) 44 in Broca's region, anterior insula, basal ganglia, cerebellum, premotor cortex (PMC, BA 6) and primary motor cortex (M1, areas 4a/4p). Dynamic causal modelling (DCM) indicated the highest evidence for a system architecture featuring the insula in a serial position between BA 44 and two parallel nodes (cerebellum/basal ganglia), from which information converges onto the PMC and finally M1. Parameter inference revealed that effective connectivity from the insular relay into the cerebellum/basal ganglia is primarily task driven (preparation) while the output into the cortical motor system strongly depends on the actual word production rate (execution). DCM hence allowed not only a quantitative characterization of the human speech production network, but also the distinction of a preparatory and an executive subsystem within it. The proposed model of physiological integration during speech production may now serve as a reference for investigations into the neurobiology of pathological states such as dysarthria and apraxia of speech.

A cooperative approach for the diagnosis of the melanoma

In this work we present a decision support system for the melanoma diagnosis using individual methods and the collaboration between these methods. The system designed uses a photograph of the lesion and it makes a preprocessing task to extract the region of interest. Then, several characteristics of the image are analyzed, studying with different methods the degree of malignity; the methods used are based in Bayesian rules and in neural networks. Finally, each individual decision from each method contributes in some way to the final decision. The classification rate obtained with the cooperative approach is above 92%.