Decision making based on quality-of-information a clinical guideline for chronic obstructive pulmonary disease scenario

Springer - Series Advances in Intelligent and Soft Computing, vol. 79In this work we intend to advance towards a computational model to hold up a Group Decision Support System for VirtualECare, a system aimed at sustaining online healthcare services, where Extended Logic Programs (ELP) will be used for knowledge re-presentation and reasoning. Under this scenario it is possible to evaluate the ELPs making in terms of the Quality-of-Information (QoI) that is assigned to them, along the several stages of the decision making process, which is given as a truth value in the interval 0…1, i.e., it is possible to provide a measure of the value of the QoI that supports the decision making process, an end in itself. It will be also considered the problem ofQoI evaluation in a multi-criteria decision setting, being the criteria to be fulfilled that of a Clinical Guideline (CG) for Chronic Obstructive Pulmonary Disease

Mobile monitoring application to support sustainable behavioural change towards healthy lifestyle

We describe the development of body area networks (BANs) incorporating sensors and other devices to provide intelligent mobile services in healthcare and well-being. The first BAN applications were designed to simply transmit biosignals and display them remotely. Further developments include analysis and interpretation of biosignals in the light of context data. By including feedback loops, BAN telemonitoring was also augmented with teletreatment services. Recent developments include incorporation of clinical decision support by applying techniques from artificial intelligence. These developments represent a movement towards smart healthcare, making health BAN applications more intelligent by incorporating feedback, context awareness, personalization, and decision support.\ud The element of decision support was first introduced into the BAN health and well-being applications in the Food Valley Eating Advisor (FOVEA) project. Obesity and overweight represent a growing threat to health and well-being in modern society. Physical inactivity has been shown to contribute significantly to morbidity and mortality rates, and this is now a global trend bringing huge costs in terms of human suffering and reduction in life expectancy as well as uncontrolled growth in demand on healthcare services. Part of the solution is to foster healthier lifestyle. A major challenge however is that exercise and dietary programs may work for the individual in the short term, but adherence in the medium and long term is difficult to sustain, making weight management a continuing struggle for individuals and a growing problem for society, governments, and health services. Using ICT to support sustainable behavioral change in relation to healthy exercise and diet is the goal of the FOVEA monitoring and feedback application. We strive to design and develop intelligent BAN-based applications that support motivation and adherence in the long term. We present this healthy lifestyle application and report results of an evaluation conducted by surveying professionals in related disciplines

Intelligent Automated Small and Medium Enterprise (SME) Loan Application Processing System Using Neuro-CBR Approach

Developing a group of diverse and competitive small and medium enterprises (SMEs) is a central theme towards achieving sustainable economic growth. SMEs are crucial to the economic growth process and play an important role in the country's overall production network. The focus of this study is to develop an automated decision support model for SMEs sector that can be used by the management to accelerate the loan application processing. This study proposed an intelligent automated SME loan application processing system (i-SMEs) that is a web based application system for processing and monitoring SME applications using Hybrid Intelligent technique which integrate Neural Network and Case-based Reasoning namely NeuroCBR. i-SMEs is used to assist SME bank management in order to improve decision making time processing as well as operational cost. i-SMEs be able to classify SME market segment into three distinctive groups that are MICRO, MEDIUM and SMALL and also can make a pre-approval loan processing faster. It is possible to transform the patterns generated from i-SME into actionable plans that are likely to help the SME Bank

Assembling Algorithmic Decision-Making under Uncertainty: The Case of \u27Edge Cases\u27 in an Open Data Environment

Algorithmic decision-making is rapidly evolving as a source of data-driven competitive advantage with important implications for analytical practices in multiple settings. Despite the ambitions for algorithmic and intelligent technologies, however, the requirement for quality data input to the algorithm poses a significant challenge for its actual adoption. The trend towards open data might bring additional challenges such as strategic gaming and distortion of meaning. To address this problem, we draw on a two-year long qualitative case study of a firm in international maritime trade to understand the role of uncertainty associated with open data upon the uptake of a novel algorithm. We combine an uncertainty and assemblage perspective to unpack the arrangements by which the organization configures relations of humans and machine to mitigate this problem. We highlight the phenomenon of edge cases as a key challenge for automation and propose that an assemblage of augmentation and automation allows a dynamic arrangement that support the introduction and organization of algorithmic decision-making under uncertainty

Explainable intelligent environments

The main focus of an Intelligent environment, as with other applications of Artificial Intelligence, is generally on the provision of good decisions towards the management of the environment or the support of human decision-making processes. The quality of the system is often measured in terms of accuracy or other performance metrics, calculated on labeled data. Other equally important aspects are usually disregarded, such as the ability to produce an intelligible explanation for the user of the environment. That is, asides from proposing an action, prediction, or decision, the system should also propose an explanation that would allow the user to understand the rationale behind the output. This is becoming increasingly important in a time in which algorithms gain increasing importance in our lives and start to take decisions that significantly impact them. So much so that the EU recently regulated on the issue of a “right to explanation”. In this paper we propose a Human-centric intelligent environment that takes into consideration the domain of the problem and the mental model of the Human expert, to provide intelligible explanations that can improve the efficiency and quality of the decision-making processes.EC - European Commission(39900 - 31/SI/2017). Northern Regional Operational Program, Portugal 2020 and European Union, trough European Regional Development Fund (ERDF) in the scope of project number 39900 - 31/SI/2017, and by FCT - Fundação para a Ciência e a Tecnologia, through projects UIDB/04728/2020 and UID/CEC/00319/201

Reinforcement machine learning for predictive analytics in smart cities

The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller ( QC ) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework

Industry 5.0 and the circular economy: utilizing LCA with intelligent products

While the move towards Industry 4.0 has motivated a re-evaluation of how a manufacturing organization should operate in light of the availability of a new generation of digital production equipment, the new emphasis is on human worker inclusion to provide decision making activities or physical actions (at decision nodes) within an otherwise automated process flow; termed by some authors as Industry 5.0 and seen as related to the earlier Japanese Society 5.0 concept (seeking to address wider social and environmental problems with the latest developments in digital system, artificial Intelligence and automation solutions). As motivated by the EU the Industry 5.0 paradigm can be seen as a movement to address infrastructural resilience, employee and environmental concerns in industrial settings. This is coupled with a greater awareness of environmental issues, especially those related to Carbon output at production and throughout manufactured products lifecycle. This paper proposes the concept of dynamic Life Cycle Assessment (LCA), enabled by the functionality possible with intelligent products. A particular focus of this paper is that of human in the loop assisted decision making for end-of-life disassembly of products and the role intelligent products can perform in achieving sustainable reuse of components and materials. It is concluded by this research that intelligent products must provide auditable data to support the achievement of net zero carbon and circular economy goals. The role of the human in moving towards net zero production, through the increased understanding and arbitration powers over information and decisions, is paramount; this opportunity is further enabled through the use of intelligent products

Smart Performance Measurement Tool in Measuring The Readiness of Lean Higher Education Institution

The development of autonomy University drives management innovation to increase the alternative sources of income with the purpose of the efficiency improvement and productivity of the institution. One of a management model that leads to increase productivity through cost reduction is Lean service. The implementation of Lean Higher Education Institution (LHEI) requires total involvement of organization maneuver, including social culture, infrastructure, and leadership support. Therefore, the readiness of the institution in welcoming Lean concepts becomes significant. This article tried to develop a prototype of an intelligent performance measurement tool by analyzing the readiness indicators using the Analytical Hierarchy Process (AHP) method. This tool provided the classification of organizational readiness into five performances level. The measurement performed as a Decision Support System (DSS) to recommend University management level in making a decision and correcting action towards the optimal execution of Lean service. As a case study, this prototype system has been tested with Black Box and User Acceptance Test (UAT) in Indonesia Islamic Higher Education Institution. The finding reveals that the prototype system can be used as a performance measurement tool in measuring the readiness of Lean's service in Islamic Higher Education Institution

Towards Human-AI Interaction in Medical Emergency Call Handling

Call-takers in emergency medical dispatch centers typically rely on decision-support systems that help to structure emergency call dialogues and propose appropriate responses. Current research investigates whether such systems should follow a hybrid intelligent approach, which requires their extension with interfaces and mechanisms to enable an interaction between call-takers and artificial intelligence (AI). Yet unclear is how these interfaces and mechanisms should be designed to foster call handling performances while making efficient use of call-taker's often strained mental capacities. This paper moves towards closing this gap by 1) deriving required artifacts for human-AI interaction and 2) proposing an iterative procedure for their design and evaluation. For 1), we apply the guidelines for human-AI interaction and conduct workshops with domain experts. For 2), we argue that performing a full evaluation of the artifacts is too extensive at earlier iterations of the design process, and therefore propose to enact use-case-driven lightweight evaluations instead