Методика инфологического анализа семантического содержания обращений пациентов для организации электронной записи

The method for patient reception processing based on infological system is proposed in the context of infological approach. This method allows organizing electronic queue for specialist attendance in health care facilities by semantic evaluation of patient health complaints.В рамках инфологического подхода, в целях автоматизации процесса записи больных в медицинских учреждениях, предложена методика обработки обращений пациентов на основе применения инфологической системы, позволяющая организовать электронную запись больных к специалистам медицинского учреждения путем выявления семантического содержания жалоб на состояние здоровья

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions

A Metadesign Theory for Tailorable Decision Support

Despite years of decision support systems (DSS) research, DSS artifacts are frequently criticized for lacking practitioner relevance and for neglecting configurability and contextual dynamism. Tailoring in end-user contexts can produce relevant emergent DSS artifacts, but design theory for this is lacking. Design science research (DSR) has important implications for improving DSS uptake, but generally this has not been promoted in the form of metadesigns with design principles applicable to other DSS developments. This paper describes a metadesign theory for tailorable DSS, generated through action design research studies in different primary industries. Design knowledge from a DSS developed in an agricultural domain was distilled and generalized into a design theory comprising: (1) a general solution concept (metadesign), and (2) five hypothesized design principles. These were then instantiated via a second development in which the metadesign and design principles were applied in a different domain (forestry) to produce a successful DSS, thus testing the metadesign and validating the design principles. In addition to contributing to DSR and illustrating innovation in tailorable technology, the paper demonstrates the utility of action design research to support theory development in DSS design

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen