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In this paper, we open up more possibilities to define higher types of fuzzy sets in a mixed way. In doing so, we show that there are essentially two alternative definitions for fuzzy sets of higher types, one of which is widely adopted in the literature, and one where the unit interval, or subsets thereof, is used to represent membership values. The other alternative definition opens up new perspectives for the use of fuzzy sets of higher types, and it promotes the use of other algebraic structures of sets of membership values, where quantales are seen as particularly useful, also in applications. The paper also underlines the importance of making distinctions between “computing with fuzzy” and “fuzzy computing” and understanding the difference between “logic with fuzzy” and “fuzzy logic”.

In this chapter the authors compare assessment techniques in education and health. These examples are drawn from assessment for learning in secondary school as compared with assessment of functioning in care of older people. Content in these respective assessment environments is obviously quite different, but there is some overlap, in particular related to social circumstances. The authors further argue that the assessment structure and formal analysis techniques are and even should be quite similar. These two domains of assessments share not only a common information and terminological structure, but also a common assessment framework (CAF) which is shown to be extendable by process detail. In this focus on assessment in health, particularly in active and healthy ageing, the model is intended to support the integration of information and processes (decision and intervention pathways) supporting various types of providers.

This paper analyzes data from coronary artery bypass grafting (CABG) using decision functions to represent rules. The data was collected at the University Hospital in Umeå, Sweden. The data contains pre-, intra-, and postoperative detail from 2975 heart operations during 1993-96. Each instance is represented by 14 preoperative variables, 4 intraoperative variables, and 9 postoperative variables. A logical method is used to predict the postoperative variables using preoperative variables. First, each postoperative variable is represented as a decision functions of preoperative variables. Then, for each postoperative variable, a minimal set of preoperative variables is derived. And finally, each postoperative variable is represented by a minimum set of rules using preoperative variables. With this method we can predict postoperative outcome, where prediction using preoperative data only is of particular interest e.g. for surgery scheduling.

Introduction: Data from mHealth apps can provide valuable information on rhinitis control and treatment patterns. However, in MASK-air®, these data have only been analyzed cross-sectionally, without considering the changes of symptoms over time. We analyzed data from MASK-air® longitudinally, clustering weeks according to reported rhinitis symptoms. Methods: We analyzed MASK-air® data, assessing the weeks for which patients had answered a rhinitis daily questionnaire on all 7 days. We firstly used k-means clustering algorithms for longitudinal data to define clusters of weeks according to the trajectories of reported daily rhinitis symptoms. Clustering was applied separately for weeks when medication was reported or not. We compared obtained clusters on symptoms and rhinitis medication patterns. We then used the latent class mixture model to assess the robustness of results. Results: We analyzed 113,239 days (16,177 complete weeks) from 2590 patients (mean age ± SD = 39.1 ± 13.7 years). The first clustering algorithm identified ten clusters among weeks with medication use: seven with low variability in rhinitis control during the week and three with highly-variable control. Clusters with poorly-controlled rhinitis displayed a higher frequency of rhinitis co-medication, a more frequent change of medication schemes and more pronounced seasonal patterns. Six clusters were identified in weeks when no rhinitis medication was used, displaying similar control patterns. The second clustering method provided similar results. Moreover, patients displayed consistent levels of rhinitis control, reporting several weeks with similar levels of control. Conclusions: We identified 16 patterns of weekly rhinitis control. Co-medication and medication change schemes were common in uncontrolled weeks, reinforcing the hypothesis that patients treat themselves according to their symptoms.

This chapter shows how assessment values often are viewed as part of a numeric scale, where in fact the fact values are understood more as symbolic in an ordinary scale. Computing with numeric and symbolic values is different, and challenging in particular if and when computations have to be intertwined. Computing with numeric values involves traditional applied and numerical mathematics, whereas computing with symbolic values involves logic and algebra. In this chapter the authors illuminate these aspects using examples from classification of functioning. 

In this chapter, the authors describe an architecture for reinforcement learning in social media marketing. The rule bases used for action selection within the architecture build upon many-valued (fuzzy) logic. Action evaluation and internal learning is based on neural network like structures. In using variables measuring the effect of advertising, we must understand direction of influence between advertiser, owning the content of the advertisement, and advertisee, as the target of an advertisement, and as facilitated by social media marketing. Examples are drawn from Facebook marketing.

We consider aggregation of fuzzy relational erosion and fuzzy relational dilation under different dual pairs of aggregation operators. Special attention is paid to the problem of preservation of duality and adjunctness under aggregation process.

Background: Different treatments exist for allergic rhinitis (AR), including pharmacotherapy and allergen immunotherapy (AIT), but they have not been compared using direct patient data (i.e., “real-world data”). We aimed to compare AR pharmacological treatments on (i) daily symptoms, (ii) frequency of use in co-medication, (iii) visual analogue scales (VASs) on allergy symptom control considering the minimal important difference (MID) and (iv) the effect of AIT.

Methods: We assessed the MASK-air® app data (May 2015–December 2020) by users self-reporting AR (16–90 years). We compared eight AR medication schemes on reported VAS of allergy symptoms, clustering data by the patient and controlling for confounding factors. We compared (i) allergy symptoms between patients with and without AIT and (ii) different drug classes used in co-medication.

Results: We analysed 269,837 days from 10,860 users. Most days (52.7%) involved medication use. Median VAS levels were significantly higher in co-medication than in monotherapy (including the fixed combination azelastine-fluticasone) schemes. In adjusted models, azelastine-fluticasone was associated with lower average VAS global allergy symptoms than all other medication schemes, while the contrary was observed for oral corticosteroids. AIT was associated with a decrease in allergy symptoms in some medication schemes. A difference larger than the MID compared to no treatment was observed for oral steroids. Azelastine-fluticasone was the drug class with the lowest chance of being used in co-medication (adjusted OR = 0.75; 95% CI = 0.71–0.80).

Conclusion: Median VAS levels were higher in co-medication than in monotherapy. Patients with more severe symptoms report a higher treatment, which is currently not reflected in guidelines.

Background: Validated combined symptom-medication scores (CSMSs) are needed to investigate the effects of allergic rhinitis treatments. This study aimed to use real-life data from the MASK-air® app to generate and validate hypothesis- and data-driven CSMSs.

Methods: We used MASK-air® data to assess the concurrent validity, test-retest reliability and responsiveness of one hypothesis-driven CSMS (modified CSMS: mCSMS), one mixed hypothesis- and data-driven score (mixed score), and several data-driven CSMSs. The latter were generated with MASK-air® data following cluster analysis and regression models or factor analysis. These CSMSs were compared with scales measuring (i) the impact of rhinitis on work productivity (visual analogue scale [VAS] of work of MASK-air®, and Work Productivity and Activity Impairment: Allergy Specific [WPAI-AS]), (ii) quality-of-life (EQ-5D VAS) and (iii) control of allergic diseases (Control of Allergic Rhinitis and Asthma Test [CARAT]).

Results: We assessed 317,176 days of MASK-air® use from 17,780 users aged 16-90 years, in 25 countries. The mCSMS and the factor analyses-based CSMSs displayed poorer validity and responsiveness compared to the remaining CSMSs. The latter displayed moderate-to-strong correlations with the tested comparators, high test-retest reliability and moderate-to-large responsiveness. Among data-driven CSMSs, a better performance was observed for cluster analyses-based CSMSs. High accuracy (capacity of discriminating different levels of rhinitis control) was observed for the latter (AUC-ROC = 0.904) and for the mixed CSMS (AUC-ROC = 0.820).

Conclusion: The mixed CSMS and the cluster-based CSMSs presented medium-high validity, reliability and accuracy, rendering them as candidates for primary endpoints in future rhinitis trials.

The article describes some of the achievements of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA), after eight years in operation. These results were achieved thanks to the collaborative work of the action groups (AGs) and reference sites (RSs). RS regional ecosystems include key organisations committed to investing in innovation to foster active and healthy ageing. The AGs are groups of professionals committed to sharing their knowledge and skills in active and healthy ageing. This article reports on the approach used by the EIP on AHA to bring together experts and regions in identifying and addressing these challenges. Synergies between AGs offered substantial support to RSs, allowing regional health and care priorities and challenges to be identified and pursued through AG commitments. Building upon the experiences of the EIP on AHA, the Reference Sites Collaborative Network has set up a number of thematic action groups that bring together multidisciplinary experts from across Europe to address the main health and social care challenges at regional, national and European level.