1,451 research outputs found
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
Integrating formal methods into medical software development : the ASM approach
Medical devices are safety-critical systems since their malfunctions can seriously compromise human safety. Correct operation of a medical device depends upon the controlling software, whose development should adhere to certification standards. However, these standards provide general descriptions of common software engineering activities without any indication regarding particular methods and techniques to assure safety and reliability. This paper discusses how to integrate the use of a formal approach into the current normative for the medical software development. The rigorous process is based on the Abstract State Machine (ASM) formal method, its refinement principle, and model analysis approaches the method supports. The hemodialysis machine case study is used to show how the ASM-based design process covers most of the engineering activities required by the related standards, and provides rigorous approaches for medical software validation and verification
Heuristic Approaches for Generating Local Process Models through Log Projections
Local Process Model (LPM) discovery is focused on the mining of a set of
process models where each model describes the behavior represented in the event
log only partially, i.e. subsets of possible events are taken into account to
create so-called local process models. Often such smaller models provide
valuable insights into the behavior of the process, especially when no adequate
and comprehensible single overall process model exists that is able to describe
the traces of the process from start to end. The practical application of LPM
discovery is however hindered by computational issues in the case of logs with
many activities (problems may already occur when there are more than 17 unique
activities). In this paper, we explore three heuristics to discover subsets of
activities that lead to useful log projections with the goal of speeding up LPM
discovery considerably while still finding high-quality LPMs. We found that a
Markov clustering approach to create projection sets results in the largest
improvement of execution time, with discovered LPMs still being better than
with the use of randomly generated activity sets of the same size. Another
heuristic, based on log entropy, yields a more moderate speedup, but enables
the discovery of higher quality LPMs. The third heuristic, based on the
relative information gain, shows unstable performance: for some data sets the
speedup and LPM quality are higher than with the log entropy based method,
while for other data sets there is no speedup at all.Comment: paper accepted and to appear in the proceedings of the IEEE Symposium
on Computational Intelligence and Data Mining (CIDM), special session on
Process Mining, part of the Symposium Series on Computational Intelligence
(SSCI
Integration of decision support systems to improve decision support performance
Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes
MODELFY: A Model-driven Solution for Decision Making based on Fuzzy Information
There exist areas, such as the disease prevention or inclement weather protocols, in
which the analysis of the information based on strict protocols require a high level of rigor and
security. In this situation, it would be desirable to apply formal methodologies that provide these
features. In this scope, recently, it has been proposed a formalism, fuzzy automaton, that captures
two relevant aspects for fuzzy information analysis: imprecision and uncertainty. However, the
models should be designed by domain experts, who have the required knowledge for the design of
the processes, but do not have the necessary technical knowledge. To address this limitation, this
paper proposes MODELFY, a novel model-driven solution for designing a decision-making process
based on fuzzy automata that allows users to abstract from technical complexities. With this goal
in mind, we have developed a framework for fuzzy automaton model design based on a Domain-
Specific Modeling Language (DSML) and a graphical editor. To improve the interoperability and
functionality of this framework, it also includes a model-to-text transformation that translates the
models designed by using the graphical editor into a format that can be used by a tool for data analysis.
The practical value of this proposal is also evaluated through a non-trivial medical protocol for
detecting potential heart problems. The results confirm that MODELFY is useful for defining such
a protocol in a user-friendly and rigorous manner, bringing fuzzy automata closer to domain expert
Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes
Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998
Aerospace medicine and biology: A continuing bibliography with indexes (supplement 346)
This bibliography lists 134 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jan. 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance
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