IWINAC'2019: Intelligent systems for cognitive training and assessment

In recent years, the important role of good mental health and the wide-ranging consequences of mental disorders have been increasingly recognized. The World Health Organization (WHO) claims that action is necessary across sectors to promote mental health and well-being, as is stated in the third Sustainable Development Goal (SDG), which advocates the need to ensure healthy lives and promote well-being for all at all ages. Mental health is a state of well-being in which an individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and is able to contribute to his or her community. On this basis, the promotion, protection and restoration of mental health can be regarded as a vital concern, and technology can play an important role in democratizing it to reach everyone. In this context, intelligent systems for cognitive training and assessment refer to artificial intelligence (AI) methodologies and techniques used to support the promotion, protection and restoration of mental health. AI seems to play an important role in the field of cognitive training and rehabilitation, as its adaptability and interactivity increase users' motivation and participation (Karyotaki & Drigas, 2015). The information technology tools mainly used are serious games, virtual worlds and virtual reality, wearable devices, brain imaging, assistance robotics, smart environments, smart homes, smart cities, data analysis, automated learning, approximated reasoning, natural language processing and many other techniques that characterize a system as intelligent. AI provides the means for flexible, robust and inexpensive applications mainly because the modelling phase is significatively simplified, with data collection for machine learning algorithms being the costliest part. Although mental health is not only the absence of disease but also the result of a complex process that includes biological, economic, social, political and environmental factors, we will focus on the biological part, brain health, which refers to how well a person's brain functions across several areas. Cognitive impairments may include problems with attention, memory recall, planning, organizing, reasoning and problem solving and are core features of mental health conditions. While some factors affecting brain health are innate, there are many lifestyle factors that can make a difference, such as being physically active, eating healthy, controlling blood pressure, keeping your mind active, being socially connected, and so forth. Evidence is not conclusive for promoting specific interventions, but at least it is encouraging (Arcara et al., 2017; Butler et al., 2018; National Academies of Sciences, Engineering, and Medicine, 2017). Therefore, health requires a continuum of care at all stages: when a person is in good health, they follow a healthy lifestyle and guidelines for effective prevention, when a pathology is detected, accurate diagnosis is needed, and personalized treatments are applied for recovery or overcoming disability. The target audience is everyone, from healthy people who want to keep their cognitive level in good conditions to those who have an impairment and want to overcome it. Similarly, intelligent systems can cover different business models such as coaching and training of cognitive functions and skills, assessment of cognitive functions, screening and assessment of cognitive diseases and disorders, cognitive intervention and rehabilitation, online assessment & training, multisensing monitoring of cognitive assessment, and so forth. In this special issue, we focus on the multiple possibilities of using AI within the health system to contribute to the well-being of the elderly, that is, to take advantage of current technologies to improve (personalize), reduce costs and democratize healthcare to reach more of the population. We selected some key contributions from the 8th International Conference on the Interplay between Natural and Artificial Computation— IWINAC—(Almería, Spain, 3–7 June 2019) and authors were invited to produce extended versions of their papers for consideration. These papers present a sample of the possibilities of AI to improve prediction, assessment, and training of patients with cognitive impairments. Machine learning techniques are used to assess and predict different cognitive impairments. In Cano-Escalera et al. (2022), the authors deal with the prediction of delirium as the main cause for hospital admittance, and the identification of the major risk factors for delirium, some of them associated with frailty of the patients. The paper explores the construction of machine learning based predictors from a broad set of demographic, clinical, and pharmacological variables. Building predictive models based on machine learning provides an alternative way to identify risk factors as the most important variables to improve predictive performance. They have found a strong association with frailty and dementia indices, and have also found strong pharmacological risks, especially the use of neuroleptics. A technique that has been frequently used to identify cognitive impairment is the monitoring of individuals' behaviour. In Gonzalez et al. (2022), the authors describe a novel method for the detection of anomalous behaviours by monitoring the power consumption of household appliances. The analysis of their daily consumption on normal days allows them to model the normal behaviour of the daily activity of people within that household. The detection of an anomalous consumption will trigger an alarm indicating that the behaviour is not the usual one, in order to communicate this incidence to those in charge. In this paper, auto-encoders and variational auto-encoders, which have a similar topological representation, are compared. From the corresponding latent layers, a classification algorithm based on Random Forest has been implemented. DOI: 10.1111/exsy.12965