Towards Adaptive Decision Support Systems: A Service-oriented Approach

Routine blood tests drive diagnosis, prognosis, and monitoring in traditional clinical decision support systems. As a routine diagnostic tool with standardized laboratory workflows, clinical blood analysis offers superior accessibility to a comprehensive assessment of physiological parameters. These parameters can be integrated and automated at scale, allowing for in-depth clinical inference and cost-effectiveness compared to other modalities such as imaging, genetic testing, or histopathology. Herein, we extensively review the analytical value of routine blood tests leveraged by artificial intelligence (AI), using the ICD-10 classification as a reference. A significant gap exists between standard disease-associated features and those selected by machine learning models. This suggests an amount of non-perceived information in traditional decision support systems that AI could leverage with improved performance metrics. Nonetheless, AI-derived support for clinical decisions must still be harmonized regarding external validation studies, regulatory approvals, and clinical deployment strategies. Still, as we discuss, the path is drawn for the future application of scalable artificial intelligence (AI) to enhance, extract, and classify patterns potentially correlated with pathological states with restricted limitations in terms of bias and representativeness.

Integration of simulation modeling and inductive learning in an adaptive decision support system

Big Data are having a great impact on the cities morphology and urban planning; actually, big data are used, for example, to predict the needs of the public transport system, by targeting investment and mobility policies within the main metropolis. Based on this assumption, this paper presents a Decision Support System (DSS) framework aimed at proposing travel strategies alternative to car use by elaborating a large amount of transportation systems data coming from different devices. The paper focalizes on the use of Big Data in public transportation and introduces a methodological framework for collecting, integrating, aggregating, fusing, managing and disseminating data coming from different sources. Data mining approaches are applied to allow the analysis of both open Big Data, that conforms to established standards such as SIRI and GTFS, and unstructured freely available Big Data. Experimental data was collected from both a sample of smartphones and AVL systems in a case study area. The application of the proposed DSS allowed analyzing spatial and temporal coverage of the public transport services, and suggesting policies to improve the modal split.

The chapter gives a background information on decision support systems and introduces a framework for building dynamic, adaptive decision support systems that evolve their structure and improve their knowledge-base through a continuous learning from data and through interaction with the environment. Such decision support systems use evolving connectionist systems (ECOS) — adaptive learning neural network models, thus their name — evolving connectionist based decision systems ECBDS. Case studies of ECBDS are presented from the areas of finance, economics, and bioinformatics.Keywordsdecision support systemsneural networksadaptive learningrule based systemsevolving connectionist systems

Forest ecosystems provide several goods and services, but strategies for the conservation of biodiversity are missing in traditional forest management schemes. In this paper we develope a decision support system to optimize the conservation of biodiversity in managed forests, taking Dadia National Park as a case study area, a local Mediterranean hotspot of biodiversity in northeastern Greece. Using environmental niche factor analysis, we produced a series of spatially explicit habitat suitability models for vascular plants, amphibians, small birds and raptors and an overall model for total biodiversity. Further, we produced maps related to timber production and investigated potential conflicts between conservation of biodiversity and wood production. A decision support system based on a conflict assessment was created using three management scenarios. It enables the establishment of integrated management strategies and the assessment of their effects on biodiversity and timber production. Habitat suitability models for selected groups of organisms were found very effective to investigate the impact of the management on forests and wildlife. Further evaluation of key indicator taxa on these models could improve decision support systems and the sustainable management of forests.

It is generally accepted that adaptive design methods and flexible constructs facilitate the design and implementation of decision support systems. Flexible constructs in the form of decision support system generators have been proposed for the timely construction of specific decision support systems. Effective communication, however, underlies the success of applying adaptive design methods and creating and modifying decision support system generators. Accordingly, we propose to investigate communication as a mode of action that allows the participants to structure and to exert control over the design environment. A descriptive model is presented that describes how different modes of communicative action are used to enhance the prospects of success when building decision support systems.

This paper aims to introduce the Decision Support System (DSS), which is an area informatics approach to area studies, and the research network complex based on DSS's application in Thailand. In particular, the paper presents and discusses current activities of the DSS research network at the national level in Thailand rather than being a theoretical and analytical study of the DSS mechanism or its cases. DSS has wide applications in Thailand, extending to various fields such as agricultural and natural resource management, economic and social management, historical and cultural preservation, and so on. DSS generally refers to a support system embedded into a computer system for providing intellectual resources with the appropriate numerical model necessary for decision making in production activities. Agricultural and natural resources have been the foundation of social and economic development in Thailand since the country's first national plan in the1960s. Decision making to maintain agricultural productivity as well as to protect natural resources requires well-integrated data sets. The Thailand Research Fund(TRF) established a DSS research and development network (TRF-DSS) in 2002 to help various research teams in Thailand develop DSS tools and components for addressing agricultural and natural resource management issues. Using a "systems"approach, the DSS framework allows researchers and users to identify and integrate key components as well as to define databases and model-base management systems. This paper focuses on the TRF-DSS research and development network, which consists of 12 universities, two line agencies in Thailand, and a line agency in Cambodia. During 2002–10, a total of 59 research projects were granted a budget allocation of 140.1 million baht, and a budget of 15.4 million baht was allocated to support activities of the network. In addition, 10 projects were funded to carry out post-project activities, with a total budget of 1.5 million baht. More than 20 DSS tools were developed and implemented by various users, ranging from policy makers to provincial and local government agencies engaged in short- and long-term planning and management. Most DSS tools were designed to allow the integration of biophysical and socioeconomic data as well as the decision support modules for alternatives evaluation and analysis. These approaches support the choices of dynamic simulation models as well as multi-criteria analyses for modelbase software development. They also allow users to evaluate various alternatives in agricultural and natural resource management. Networking is a powerful platform for DSS research and development and maybe applied to other types of research and development agenda in Thailand, such as area study projects. DSS tools contribute to an understanding of sustainability of production systems against a background of climate change, poverty reduction, and food security.

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Business organisations are increasing their influence and presence throughout the world as domestic economies become oversaturated. Organisations increasingly turn to international commerce to discover new markets. Globalisation requires that firms adapt their management and decision-making practices to recognise localised cultural, political, legal, social, and security requirements. In response, organisations are revamping their decision processes, tailoring them as cultures evolve, political assumptions and realities change, laws are enacted, social issues come to the forefront, and security requirements mature. Traditional decision support systems (DSS) are incapable of readily incorporating localised variables into decision-making processes and must be enhanced. Geographic Information Systems (GIS), have the potential to enhance traditional management decision-making (DSS) tools by adapting them for use in global environments. This paper proposes the melding of GIS and DSS toolsets to create a new framework, hereinafter referred to as Geographically-Enhanced Decision Support Systems (GEDSS), for enhanced management and decision-making for businesses.

As businesses grow across continents in terms of reach, scale, and diversity in products, the complexity of management problems and efforts to maximize the profit in corporations have increased significantly. To assist managers and employees at all levels in decision making, corporate modeling is essential to provide decision support information. Corporate modeling within business organizations has moved from standalone database applications to integrated and flexible enterprise-wide information systems. Computer modeling and planning tools such as decision support systems (DSS) are widely used by corporate management to select alternatives based on key business performance measures. Enterprise resource planning (ERP) systems have also been introduced as a new form of corporate modeling that does the same thing as traditional DSS, but they are not confined to the boundaries of business applications. This article reviews the various types of corporate models along with development and applications of advanced DSS and ERP systems. Keywords: corporate modeling; customer-centric enterprise information systems; decision support systems; enterprise resource planning; evolutionary multiagent systems

It is shown that, although many aspects of a traditional decision support system (TDSS) and an organizational decision support system (ODSS) are similar, there are important differences that must be understood if the ODSS is to be successful. Differences in purposes lead to differences in design, differences in managing the development effort, differences in implementation, and differences in maintenance. In fact, some aspects of building an ODSS are more similar to building a traditional information system than to building a TDSS. The suggested approach to building an ODSS combines principles and good practice for building both types of systems. The points made are illustrated with examples from the ODSS that are currently being implemented by the US Air Force. The system, called the Enlisted Force Management System, is being used to help members of the Air Staff in the Pentagon make decisions related to their enlisted personnel. >

The continued growth in the volume of available domain and technical data has been facilitated by a corresponding advancement in and communication technology. This information overload can result in inefficient use of time and resources as well as the creation of recommended courses of action that are overruled by the decision makers' judgment and experience. In order to address these problems, multiple knowledge sources and an inference process capable of mirroring the human thought processes (especially judgment and experience) must be available at the right time to the persons or groups needing the knowledge for decision making. Such a concept can be referred to as MANAGED QUALITATIVE THINKING SUPPORT SYSTEMS (MQTSS). Traditional decision support systems (DSS) rely upon decision maker or staff expertise to render knowledge in support of decision making. If the decision maker or staff has insufficient domain or technical expertise to utilize the DSS's embedded models, interpret results, or implement the recommendations, knowledge delivery may be compromised or rendered ineffective. MQTSS can alleviate these support problems and improve knowledge delivery for decision making by reducing knowledge search times, streamlining decision-making tasks, reducing decision time, and promoting appropriate qualitative thinking,. The MQTSS approach theoretically can enhance the decision making process and decision outcomes. This paper attempts to replicate an earlier study by this author to test the theory. First, the MQTSS approach is presented. Next, an system is created to deliver the technology to management. Finally, a simulation experiment is reported that compares the effectiveness of support rendered by a traditional decision support system and the created MQTSS system. The paper closes with conclusions and implications for systems research.

Adopting Artificial Intelligence in a Decision Support System – Learnings from Comment Moderation Systems

In this paper, we are motivated to assist business executives and managers especially strategic planning manager in making decisions, which in turn helping the organization to achieve its objective which is to maximise the profit of business. A framework of decision support system (DSS), which suggests optimal strategic actions to be performed, is proposed. The idea is to integrate Finite State Transducer (FST) and fuzzy logic engine in DSS, with ability to capture vague natural language provided by decision makers. The DSS design decisions and business rules are constructed by decision makers based on business prioritised goals. In the proposed framework, the Stochastic Moore Machine (SMM) model, a modified version of FST based on Edward F. Moore is designed with the focus on the non-deterministic behaviour of the state transitions. The machine is integrated with fuzzy logic engine, in the mapping of crisp input of indicators from the management system to membership value for fuzzy operation. The fuzzy value computed will be the input for the probabilistic transition using roulette wheel selection. A case study, detailing how the DSS and SMM work in a small cafe, with the business owner as the strategic planning manager, and two hired employee as operational managers is discussed to illustrate the behaviour of the model based on a triggering event. The case study illustrates how the decisions from business owner and operational managers collectively contribute towards the business goal, by suggesting actions to introduce new menu or reduce fixed expenses.

Smart manufacturing has transformed the way decisions are made. By accelerating the delivery of data to the various decision points, more rapid decision-making processes can be realized. A generic Decision Support System (DSS) utilizes an efficient technique, which integrates the algorithm for inventive problem solving (ARIZ) and supervised machine learning into a model for supporting various automated decision making processes. The proposed model is to examine the theoretical framework of ARIZ by devising an ARIZ-based DSS model. It incorporates supervised ML algorithms to assist decision making processes. Three case studies from the manufacturing sector are evaluated. The results indicate the capability of the proposed DSS in achieving a high accuracy rate and, at the same time reducing the time and resources required for decision making. Our study has simplified the data processing and extraction processes through an automated ARIZ-based DSS model; therefore enabling a non-technical user the opportunity to harvest the vast knowledge from the collected data for efficient decision making.