Prevention of fragility fractures in older people has become a public health priority, although the most appropriate and cost-effective strategy remains unclear. In the present statement, the Interest group on falls and fracture prevention of the European union geriatric medicine society (EUGMS), in collaboration with the International association of gerontology and geriatrics for the European region (IAGG-ER), the European union of medical specialists (EUMS), the Fragility fracture network (FFN), the International osteoporosis foundation (IOF) - European society for clinical and economic aspects of osteoporosis and osteoarthritis (ECCEO), outlines its views on the main points in the current debate in relation to the primary and secondary prevention of falls, the diagnosis and treatment of bone fragility, and the place of combined falls and fracture liaison services for fracture prevention in older people.
The Strategic Implementation Plan of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) proposed six Action Groups. After almost three years of activity, many achievements have been obtained through commitments or collaborative work of the Action Groups. However, they have often worked in silos and, consequently, synergies between Action Groups have been proposed to strengthen the triple win of the EIP on AHA. The paper presents the methodology and current status of the Task Force on EIP on AHA synergies. Synergies are in line with the Action Groups' new Renovated Action Plan (2016-2018) to ensure that their future objectives are coherent and fully connected. The outcomes and impact of synergies are using the Monitoring and Assessment Framework for the EIP on AHA (MAFEIP). Eight proposals for synergies have been approved by the Task Force: Five cross-cutting synergies which can be used for all current and future synergies as they consider overarching domains (appropriate polypharmacy, citizen empowerment, teaching and coaching on AHA, deployment of synergies to EU regions, Responsible Research and Innovation), and three cross-cutting synergies focussing on current Action Group activities (falls, frailty, integrated care and chronic respiratory diseases).
The Allergic Rhinitis and its Impact on Asthma (ARIA) initiative commenced during a World Health Organization workshop in 1999. The initial goals were (1) to propose a new allergic rhinitis classification, (2) to promote the concept of multi-morbidity in asthma and rhinitis and (3) to develop guidelines with all stakeholders that could be used globally for all countries and populations. ARIA-disseminated and implemented in over 70 countries globally-is now focusing on the implementation of emerging technologies for individualized and predictive medicine. MASK [MACVIA (Contre les Maladies Chroniques pour un Vieillissement Actif)-ARIA Sentinel NetworK] uses mobile technology to develop care pathways for the management of rhinitis and asthma by a multi-disciplinary group and by patients themselves. An app (Android and iOS) is available in 20 countries and 15 languages. It uses a visual analogue scale to assess symptom control and work productivity as well as a clinical decision support system. It is associated with an inter-operable tablet for physicians and other health care professionals. The scaling up strategy uses the recommendations of the European Innovation Partnership on Active and Healthy Ageing. The aim of the novel ARIA approach is to provide an active and healthy life to rhinitis sufferers, whatever their age, sex or socio-economic status, in order to reduce health and social inequalities incurred by the disease.
Allergic Rhinitis and its Impact on Asthma (ARIA) has evolved from a guideline by using the best approach to integrated care pathways using mobile technology in patients with allergic rhinitis (AR) and asthma multimorbidity. The proposed next phase of ARIA is change management, with the aim of providing an active and healthy life to patients with rhinitis and to those with asthma multimorbidity across the lifecycle irrespective of their sex or socioeconomic status to reduce health and social inequities incurred by the disease. ARIA has followed the 8-step model of Kotter to assess and implement the effect of rhinitis on asthma multimorbidity and to propose multimorbid guidelines. A second change management strategy is proposed by ARIA Phase 4 to increase self-medication and shared decision making in rhinitis and asthma multimorbidity. An innovation of ARIA has been the development and validation of information technology evidence-based tools (Mobile Airways Sentinel Network [MASK]) that can inform patient decisions on the basis of a self-care plan proposed by the health care professional.
This paper studies the problem of formalizing consensus reaching within a set of decision makers trying to find and agree upon a mutual decision. Decision makers produce their individual rankings, using their own pet decision schemas. Thus consensus reaching relies only on the aggregation of individual decisions rather than on individual decision procedures. The aggregation of the individual rankings is supported by an advising monitor which tries to contract the decision makers into a mutual decision through soft enforcement. Convergence to consensus then depends upon the decision makers' willingness to compromise. We use a topological approach to consensus where we can measure distances between decision makers. Within the approach we can also model the trade-off between a degree of consensus and a strength of majority.
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.
In this chapter we present a formal description of information management for assessment and classification in learning. The description is supported by a structure related to drama process for learning. Our logic follows the idea of invoking uncertainties using underlying categories, and the language of processes in 'drama process' is taken to be BPMN (Business Process Modelling Notation).
In this paper we show how category theoretic ontology provided by generalized general logic can be used for decision-making with assessment scales and consensus guidelines in social and health care of older people. Computerized decision-making in social and health care is traditionally views ontologies not as part of underlying logics for decision-making, but rather as standards and terminologies including skeletons and frameworks of informal logic structures. Programming in logic is manipulation of terms, and substitution with terms. Classical terms won’t suffice. An ontology building upon classical terms, trying to enhance missing parts in the underlying structures by being clever about inference, becomes logically sterile and basically useless in formal frameworks. We also need to make a distinction between imprecise or vague information, and being formal and accurate in reasoning with vague values. Furthermore, a value may be vague as produced by a crisp operation, or a value is vague since the underlying operation is vague. From formal point of view this is all about underlying categories and monads, and in this paper we will continue investigations showing how the signatures reside in term monads over chosen categories. Our approach is thus monadic, and we consider monads over suitable categories.
In this paper we discuss computerized assessments and guidelines for decision making, with accuracy and formalism being required for avoiding ambiguities and imprecision. Note the difference between imprecise or vague information, and being formal and accurate in reasoning with vague values. We may even have a logic allowing for vague reasoning, where the underlying logic is formal and precise, i.e. must always have a clear syntax and semantics together with a accurately described inference mechanism revealing the strength and weakness, and indeed the nature and capacities of that particular underlying logic. Our examples will be drawn from elderly care, in particular from assessments in old age psychiatry and early diagnosis of cognitive disorder.
Municipal and regional best practices for strategic planning and management of ageing is achieved by developing accurate socio-economic modelling tools based on rigorous design of information and processes. Demographic models enable analysis and prediction of demographic change, and socio-economic modelling, based on ageing information and process design, is sensitive and specific in particular concerning variables related to demographic change. Service forms based on observation, assessment, and decision-making are typically used in home care, adult day care centres, residential care, nursing homes, and/or wards. The suggested approach to socio-economic modelling-based strategic planning is both customer-centric with respect to information and process design as well as care-centric with respect to care management.
Cognitive decline progresses from the mild cognitive impairment stage and proceeds into various dementia types and progresses through different severities. During the course of the cognitive decline the patient is subjected to actions and decision making as provided by respective professional groups. Coordination of various clinical and care giving skills and capacities involve information management and reasoning which require consideration of different logic platforms suited for the professional groups. Particularly at early detection it is important to adapt information and knowledge representation to situations where cognitive problems are encountered and by whom these observations are made. The very first observations of cognitive decline are usually made by thepatient's husband/wife and relatives Advice is then sought from social authorities, nurses and primary care doctors within their local health care centres. Professional representation in these groups will not perform final diagnosis but rather provide necessary background information for assessment and initialization of thediagnostic procedures. Moving between logic representations of guidelines requires formalism as provided by general logics.
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.
Cooperation and partnership in healthy ageing enhances and enriches the underlying information and process models within integrated care. On information, functioning oriented data as part of health and social data describes medical conditions and functioning capacity of the older person. Similarly, the notion of a good practice, as embracing a conglomerate of guidelines, is also well understood but less so in terms of process substance. Process structure granularity is often quite coarse and less formal, comparable to process descriptions annotated with clinical guidelines. This chapter describes an algebraic framework for representation of functioning data typically found in contexts of integrated care processes in healthy ageing.
In today's world, healthy aging and a fulfilling lifestyle are important to older members of society, with many opting to remain as independent and mobile as possible for as long as possible. However, elderly individuals tend to have a variety of functional limitations that can increase the likelihood of debilitating falls and injuries. Assessments of functionality are very often only performed following an accident, which implies a hindsight bias because results do not necessarily reflect pre-accidental performance capacities. Furthermore, these belated measures do little to reduce the likelihood of new falls. As such, it is imperative that personalized preventative approaches are taken to prevent falls.
Integrated Care and Fall Prevention in Active and Healthy Aging contains state-of-the-art research and practices related to integrated care, fall prevention, and aging throughout areas ranging from medical to social aspects of care, health economy, standards, pathways and information scopes, practices and guidelines, technology, etc. Covering topics such as active care and healthy aging, it is ideal for doctors, gerontologists, nursing home and long-care facility staff, scientists, researchers, students, academicians, and practitioners working in care pathways involving good practices of fall prevention in home care and community care settings.
Given health and health economy assessments, a common assessment framework for active and healthy ageing (CAFAHA) is ideally desirable, even if not yet fully feasible, given the activities developed within European Innovation Partnership for Active and Healthy Ageing (EIP on AHA) since 2012, now moving into its subsequent framework on healthy ageing. However, as there is diversity with respect to maturity in regions, in order to fully develop prevention practices and campaigns, assets as part of maturity need to be defined more clearly.
Qualified-self aspects within AHA requires having emphasis on empowerment and how citizens as individuals and patients can manage their own data, in particular for self-monitoring purposes. For this management to meet reply properly to the societal grand challenge of AHA, there is the need to shift from society owning all individual health data to individuals themselves owning their data. Another aspect is that the Quantified-Self movement is still rooted mostly in wellness and even fitness, and as having various apps at their disposal. Focus is then not always just on health but on performance more in general.
In this paper we provide recommendations on how to use quantales as algebraic structures to represent uncertainty and many-valuedness in design engineering using relational views for connecting and combining information. Information is further detailed as based on underlying signatures of types and operators, providing expressions and terms that also become subjected to many-valued qualifications. Machine and engineering design, and related design structures usually adopt rather trivial relational models, and shallow expressions to describe various conditions. In particular, uncertainty, e.g., in prediction and risk estimation, is often based on quite rudimentary and ad-hoc probabilities of events that are mostly just named rather than described in detail. The objects in question being just named items, without elaborating on the internal structure of these objects, makes these descriptions to be simple constants, and truth valuation remains as only binary. We will show how objects can be structured, and how structured objects can be related using various algebraic structures. This enables to provide a richer model also on many-valuedness from a logical point of view. Specifically we will look at the algebraization of the Design Structure Matrix (DSM).
Circuit design requires robustness to counteract undesirable variability, as typically seen e.g. in magnetic memory devices. Approximation to discrete structures is needed so that, on the one hand, the size of the structure is not too small where richer algebraic structures cannot be integrated, and, on the other hand, not too large so that variability makes practical application unfeasible.
Computerized decision-making in social and health care is traditionally focused on representation and implementation of know-how and guidelines. Less attention has been paid to underlying data structures and formalizations of ontologies. In this paper we show how underlying signatures of a logic can be based on monads over suitable categories. Furthermore, we argue in favour of using application domain specific logic, and even cross-functional logic as enabled by general logics. Our examples are drawn from decision-making with assessment scales and consensus guidelines in social and health care of older people.
In this paper we present some views on ontologies and assessments, and the relation between logic and guidelines within municipal decision-making in elderly care. Logic is seen, on the one hand, as carrier of information, and, on the other hand, as including mechanisms for inference as underlying decision-making. The ontology and logic for the framework is based on a non-classical typing system where uncertainty is canonically developed in a category theory framework involving term monads both composed with other monads, and as viewed over other categories than just the category of sets. The main question is where uncertainty actually resides, so that they are canonically retrieved rather than amalgamated in ad hoc approaches.
In this paper we show how many-valued relations syntactically can be formulated using powertype constructors. This in turn enables to describe the syntax of generalized relations in the starting point sense where the category sets and relations is isomorphic to the Kleisli category of the powerset monad over the category of sets. We can then generalize to work over monoidal closed categories, and thereby description logic, formal concepts and rough sets can be viewed as depending on that powertype constructor, and within a setting of many-valued lambda-calculus. In order to achieve this, we will adopt a three-level arrangement of signatures [4], and demonstrate the benefits of using it. Bivalent and untyped relational adaptations typically appear in terminology and ontology, and we will illuminate this situation concerning classifications in health. Extensions to multivalent and typed nomenclatures provides an enrichment that is beneficial in practical use of health classifications and nomenclatures.
In this paper we discuss various software engineering aspects of guideline computerisation, both from domain oriented as well as technology driven points of view. The discussion includes case studies on pharmacological treatment of hypertension, diagnosis of dementia, and drug interactions.
In this paper, we show how monads and substitutions allows for a separation between social choice and social 'choosing'. Choice as value and choosing as operation is modeled using underlying signatures and related term monads. These monads are arranged over Goguen's category Set(L), which provides the internalization of uncertainty both in choice as well as choosing.
Are we interested in choice functions or function for choice? Was it my choice or did I choose? In the end it is all about sorts and operators, terms as given by the term monad over the appropriate category, and variable substitutions as morphisms in the Kleisli category of that particular term monad.
Non-linear models, such as given by neural networks and fuzzy logic, have established a good reputation for medical data analysis as computational and logical counterparts to statistical methods. Whereas multilayer perceptrons perform well with large data sets, a combination of neural learning together with fuzzy logical network interpretations provides a network reduction well suited for smaller data sets. The aim of this paper is to present an approach to neural fuzzy systems data analysis and knowledge acquisition in laboratory information systems. We also describe a software system, DiagaiD, which provides an analysis and development workbench involving laboratory data.
This paper describes a method how to arrive at a medical expert system (as a knowledge based system) to support physicians in classifying patients in diagnosis of Nephropathia epidemica (NE). We thereby present a link between Lukasiewicz inference and learning in neural nets, as a formal connection between uncertainty in logical implication and synaptic weights. The system presented uses clinical findings and laboratory investigations to arrive at predictions whether or not patients suffer from NE. Although we are willing to call our system a medical expert system, it could equally well be called a decision support system, this being more in spirit to what such a system really offers a physician.
This paper describes a clinical support systems workbench, DiagaiD, based on an efficient transfer of patient data between health care professionals and clinical subsystems. The DiagaiD workbench provides tools for decision support developments for open-loop systems.A leading ambition for our development work has been to establish a data analysis and knowledge elicitation workbench, in which medical professionals entirely by themselves can create stand-alone expert systems.
Traditionally, rough sets build upon relations based on ordinary sets, i.e. relations on X as subsets of X x X. A starting point of this paper is the equivalent view on relations as mappings from X to the (ordinary) power set PX. Categorically, P is a set functor, and even more so, it can in fact be extended to a monad (P,eta,mu). This is still not enough and we need to consider the partial order (PX, <=). Given this partial order, the ordinary power set monad can be extended to a partially ordered monad. The partially ordered ordinary power set monad turns out to contain sufficient structure in order to provide rough set operations. However, the motivation of this paper goes far beyond ordinary relations as we show how more general power sets, i.e. partially ordered monads built upon a wide range of set functors, can be used to provide what we call rough monads.
Non-standard logics departs from traditional logic mostly in extended views, on one hand syntactically related to logical operators, and on the other hand semantically related to truth values. Typical for these approaches is the remaining traditional view on 'sets and relations' and on terms based on signatures. Thus the cornerstones of the languages remain standard, and so does mostly the view on knowledge representation and reasoning using traditional substitution theories and unification styles. In previous papers we have dealt with particular problems such as generalizing terms and substitution, extending our views on sets and relations, and demonstrated the use of these non-standard language elements in various applications such as for fuzzy logic, generalized convergence spaces, rough sets and Kleene algebras. In this paper we provide an overview and summarized picture of what indeed happens when we drop the requirement for using traditional sets with relations and terms with equational settings
Rough sets and fuzzy sets are both methods to represent uncertainty. In previous work we have developed, within the abstract language of category theory, some interesting tools for providing a foundation to the development of a general framework for unification, working with powersets of terms. Monads, in this context, establish an essential concept in that they contain set functors with structure provided by natural transformations. In this paper we show how monads, extended to partially ordered monads, can be used to generalize and interpret rough situations. In particular, the partially ordered ordinary power set monad turns out to contain sufficient structure in order to provide rough set operations. This study of rough sets from a categorical view, provides an abstract tool to handle properties of the structure increasing their understanding in a basic many-valued logic setting.
Formal Concept Analysis (FCA) as inherently relational can be formalized and generalized by using categorical constructions. This provides a categorical view of the relation between "object" and "attributes", which can be further extended to a more generalized view on relations as morphisms in Kleisli categories of suitable monads. Structure of sets of "objects" and "attributes" can be provided e.g. by term monads over particular signatures, and specific signatures drawn from and developed within social and health care can be used to illuminate the use of the categorical approach.
Composing various powerset functors with the term monad gives rise to the concept of generalized terms. This in turn provides a technique for handling many-valued sets of terms in a framework of variable substitutions, thus being the prerequisite for categorical unification in many-valued logic programming using an extended notion of terms. As constructions of monads involve complicated calculations with natural transformations, proofs are supported by a graphical approach that provides a useful tool for handling various conditions, such as those well known for distributive laws. (C) 2007 Elsevier B.V. All rights reserved.
In this paper we use the concept of subfunctors and submonads in order to provide a technique for constructing new monads from given ones. We study some properties of these constructions and provide more examples on monad compositions
Categories arise in mathematics and appear frequently in computer science where algebraic and logical notions have powerful representations using categorical constructions. In this chapter we lean towards the functorial view involving natural transformations and monads. Functors extendable to monads, further incorporating order structure related to the underlying functor, turn out to be very useful when presenting rough sets beyond relational structures in the usual sense. Relations can be generalized with rough set operators largely maintaining power and properties. In this chapter we set forward our required categorical tools and we show how rough sets and indeed a theory of rough monads can be developed. These rough monads reveal some canonic structures, and are further shown to be useful in real applications as well. Information within pharmacological treatment can be structured by rough set approaches. In particular, situations involving management of drug interactions and medical diagnosis can be described and formalized using rough monads.