1,721,204 research outputs found
Guest Editorial: Temporal representation and reasoning
Full text linkIn this editorial I introduce the main topics of papers in the special issue
Editorial from the new Editor-in-Chief: Artificial Intelligence in Medicine and the forthcoming challenges
No full textIn this editorial I will share some observations, comments, and desiderata about AIIM. At the same time, I will introduce some (small) changes in the organization of the journal
Adding flexibility to uncertainty: Flexible Simple Temporal Networks with Uncertainty (FTNU)
Full text linkA Flexible Simple Temporal Network with Uncertainty (FTNU) represents temporal constraints between time-points. Time-points are variables that must be set (executed) satisfying all the constraints. Some time-points are contingent. It means that they are set by the environment and only observed by the system executing the network. The ranges repre- senting temporal constraints associated with contingent time-points (guarded ranges) can be shrunk during execution only to some extent to have more flexibility in the execution of the network. Subsets of time-points/constraints may be executed/considered in different contexts according to some observed conditions. The main issue here consists of determining whether all the time-points, under the control of the system, are executable in a way that all the specified constraints are satisfied for any possible occurrence of contingent time-points and any possible context. Such property is called controllability. Even though an algorithm was proposed for checking the controllability of such networks, we show that such an algorithm has a limit. Indeed, it does not determine the right bounds for guarded links, and, therefore, it doesn’t permit the system to exploit the potential flexibility of the network. We then propose a new constraint-propagation algorithm for checking controllability, prove that such a new algorithm determines the right guarded ranges, and it is sound-and-complete. Thus, it can be used also for executing the network, by leveraging its flexibility
Visually defining and querying consistent multi-granular clinical temporal abstractions
No full textThe main goal of this work is to propose a framework for the visual specification and query of consistent multi-granular clinical temporal abstractions. We focus on the issue of querying patient clinical information by visually defining and composing temporal abstractions, i.e., high level patterns derived from several time-stamped raw data. In particular, we focus on the visual specification of consistent temporal abstractions with different granularities and on the visual composition of different temporal abstractions for querying clinical databases
Temporal Reasoning and Temporal Data Maintenance in Medicine: Issues and Challenges. In: COMBI, C., SHAHAR, Y. (eds.) Time-Oriented Systems in Medicine. Special Issue
No full textIntroduction to the ACM TIST Special Issue on Intelligent Healthcare Informatics
Full text linkHealthcare Informatics is a research area dealing with the study and application of computer science and information and communication technology to face both theoretical/methodological and practical issues in healthcare, public health, and everyday wellness. Intelligent Healthcare Informatics may be defined as the specific area focusing on the use of artificial intelligence (AI) theories and techniques to offer important services (such as a component of complex systems) to allow integrated systems to perceive, reason, learn, and act intelligently in the healthcare arena. One of the many peculiarities of healthcare is that decision support systems need to be integrated with several heterogeneous systems supporting both collaborative work and process coordination and the management and analysis of a huge amount of clinical and health data, to compose intelligent, process-aware health information systems. After some pioneering work focusing explicitly on specific medical aspects and providing some efficient, even ad hoc, solutions, in recent years, AI in healthcare has been faced by researchers with different backgrounds and interests, taking into consideration the main results obtained in the more general and theoretical/methodological area of intelligent systems. Moreover, from a focus on reasoning strategies and deep knowledge representation, research in healthcare intelligent systems moved to data-intensive clinical tasks, where there is the need for supporting healthcare decision making in the presence of overwhelming amounts of clinical data. Significant solutions have been provided through a multidisciplinary combination of the results from the different research areas and their associated cultures, ranging from algorithms, to information systems and databases, to human-computer interaction, to medical informatics. To this regard, it is interesting to observe that, from one side, medical informaticians benefited by the general solutions coming from the generic computer science area, tailoring them to specific medical domains, while from the other side, computer scientists found several (still open) challenges in the medical and, more generally, health domains. This ACM Transactions on Intelligent Systems and Technology (ACM TIST) special issue contains articles discussing fundamental principles, algorithms, or applications for process-aware health information systems. Such articles are a sound answer to the research challenges for novel techniques, combinations of tools, and so forth to build effective ways to manage and deal in an integrated way with healthcare processes and data
Temporal Functional dependencies with multiple granularities: a logic based approach
No full textFunctional dependencies allow one to define semantic constraints on relational database schemata; even though functional dependencies do not focus on data dynamics, some efforts have been devoted in these last twenty years to extend functional dependencies to support temporal aspects of data. Different temporal functional dependencies have been proposed, which capture temporal features which are slightly different one from each other. In this paper we propose a logic based approach for the problem of modeling and managing temporal data dependencies. More particularly, we show how our proposal allows one to express in a homogeneous way the temporal functional dependencies previously proposed in the literature
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