An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry

With the increasing ageing population worldwide, providing effective nursing care planning in nursing homes is important in meeting the expectations of elderly patients and in streamlining the healthcare information process, hence maintaining high‐quality services. Instead of the traditional manual nursing care planning formulation based on expert experience and subjective judgement, this paper describes an adaptive decision support system, namely, the cloud‐based nursing care planning system, to enable decision making in formulating nursing care strategies. By integrating cloud computing technology and the case‐based reasoning (CBR) technique, medical records and documents pertaining to the elderly can be captured in real time, whereas appropriate treatment plans based on past similar treatment records can be formulated. However, the current case adaptation processes in CBR rely on domain experts to modify retrieved cases, which may not satisfy the needs of the elderly. Therefore, text mining is integrated in the case adaptation process of CBR for extracting up‐to‐date medical information from the Internet so that its efficiency can be improved. By conducting a pilot study in a nursing home, it was shown that the time for formulating applicable treatment plans for elderly patients can be reduced, and the service satisfaction level can be enhanced.     

Data envelopment analysis based on team reasoning

Existing approaches to data envelopment analysis focus mainly on the derivation of the efficiency of the individual decision‐making unit (DMU) or on the calculation of the weights of multiple inputs or outputs, but pay little attention to the team interest of all the DMUs. Motivated by the idea of team reasoning, in which the benefit of the team is of higher importance than that of each individual, this paper considers all the DMUs as a team and introduces the team indexes including the overall efficiencies, variance, boundaries of all the DMUs, and relationships between DMUs. Several models are first developed to estimate values of the team indexes based on which decision makers can provide their preferences regarding them. Then, models are established to obtain the interval efficiencies of individual DMUs under the condition that the team indexes are satisfied. Several examples are given to illustrate the proposed approaches and verify their applicability.     

Evidential reasoning-based Fuzzy AHP approach for the evaluation of design alternatives’ environmental performances

With the growing general awareness of the need to protect the environment as well as the increasingly stringent regulatory requirements imposed by various national and cross-national bodies, manufacturers have to minimise the environmental impacts of their products. Environmental considerations have therefore become a new key criterion for evaluating design alternatives during the product development stage. To facilitate non-Life Cycle Assessment (LCA) experts, such as most product designers, in evaluating the design alternatives in terms of environmental friendliness, this paper introduces a decision-making mechanism that combines the multiple criteria decision making (MCDM) approaches with LCA methodology. This evidential reasoning-based approach is a fast-track and objective tool which ranks the available design alternatives according to their potential environmental impacts. The environmental impacts of design alternatives assessed by the LCA are used for the weight elicitation processes of the proposed approach. A case application is conducted to illustrate the use of the proposed method to evaluate the environmental performances of design alternatives.     

From Business Intelligence to semantic data stream management

The Semantic Web technologies are being increasingly used for exploiting relations between data. In addition, new tendencies of real-time systems, such as social networks, sensors, cameras or weather information, are continuously generating data. This implies that data and links between them are becoming extremely vast. Such huge quantity of data needs to be analyzed, processed, as well as stored if necessary. In this position paper, we will introduce recent work on Real-Time Business Intelligence combined with semantic data stream management. We will present underlying approaches such as continuous queries, data summarization and matching, and stream reasoning.     

SIMILARITY‐BASED RETRIEVAL WITH STRUCTURE‐SENSITIVE SPARSE BINARY DISTRIBUTED REPRESENTATIONS

We present an approach to similarity‐based retrieval from knowledge bases that takes into account both the structure and semantics of knowledge base fragments. Those fragments, or analogues, are represented as sparse binary vectors that allow a computationally efficient estimation of structural and semantic similarity by the vector dot product. We present the representation scheme and experimental results for the knowledge base that was previously used for testing of leading analogical retrieval models MAC/FAC and ARCS. The experiments show that the proposed single‐stage approach provides results compatible with or better than the results of two‐stage models MAC/FAC and ARCS in terms of recall and precision. We argue that the proposed representation scheme is useful for large‐scale knowledge bases and free‐structured database applications.     

PRACTICAL INCONSISTENCY MANAGEMENT FOR CRITICAL-TASKS DECISION-SUPPORT SYSTEMS

Abstract

In this paper, the problem of logical consistency in critical-tasks decision systems is addressed. It is shown how local search techniques can be used to check the (in)consistency of very large propositional knowledge bases (KBs). Using new heuristic findings. we can now implement efficient forms of diagnostic reasoning in the context of very large ICBs modeling deep models of complex devices and including patterns of default reasoning.     

FUZZY REASONING USING POSITIONAL INFORMATION

Abstract

The paper argues in favour of reasoning methods that process positional information, i.e. information about the position of the input with respect to the class prototype, and thus achieve a better specification of the corresponding output class. It is shown that such an approach leads to reducing or cancelling unwanted nonlinearities (of input-output characteristic) imposed by reasoning methods, nonlinearities that alter the dependence expressed in the knowledge base. Two methods which use positional information are presented: (a) the mapping of o-cut borders (MACB) (Stoica, 1993, Proc. Fifth IFSA World Congress, pp. 1266-1269), inspired from a particular interpretation of the representation theorem and (b) the Left/Right Centre of Gravity (L/R COG) (Ghanayem, 1995, Proc. IEA/AIE, pp. 41 -45). The methods are tested in implementing a power system stabiliser (PSS) Simulation results show better performance of methods that use positional information, as compared with classic Mamdani (Mamdani, 1975, International Journal of Man-Machine Studies, 7, 1-13) reasoning. Reflections on a “best” reasoning method conclude the paper.