ترغب بنشر مسار تعليمي؟ اضغط هنا

Edhibou: a Customizable Interface for Decision Support in a Semantic Portal

245   0   0.0 ( 0 )
 نشر من قبل Fadi Badra
 تاريخ النشر 2008
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English
 تأليف Fadi Badra




اسأل ChatGPT حول البحث

The Semantic Web is becoming more and more a reality, as the required technologies have reached an appropriate level of maturity. However, at this stage, it is important to provide tools facilitating the use and deployment of these technologies by end-users. In this paper, we describe EdHibou, an automatically generated, ontology-based graphical user interface that integrates in a semantic portal. The particularity of EdHibou is that it makes use of OWL reasoning capabilities to provide intelligent features, such as decision support, upon the underlying ontology. We present an application of EdHibou to medical decision support based on a formalization of clinical guidelines in OWL and show how it can be customized thanks to an ontology of graphical components.



قيم البحث

اقرأ أيضاً

After presenting the broad context of authority sharing, we outline how introducing more natural interaction in the design of the ground operator interface of UV systems should help in allowing a single operator to manage the complexity of his/her ta sk. Introducing new modalities is one one of the means in the realization of our vision of next- generation GOI. A more fundamental aspect resides in the interaction manager which should help balance the workload of the operator between mission and interaction, notably by applying a multi-strategy approach to generation and interpretation. We intend to apply these principles to the context of the Smaart prototype, and in this perspective, we illustrate how to characterize the workload associated with a particular operational situation.
126 - Arnaud Martin 2008
The textured images classification assumes to consider the images in terms of area with the same texture. In uncertain environment, it could be better to take an imprecise decision or to reject the area corresponding to an unlearning class. Moreover, on the areas that are the classification units, we can have more than one texture. These considerations allows us to develop a belief decision model permitting to reject an area as unlearning and to decide on unions and intersections of learning classes. The proposed approach finds all its justification in an application of seabed characterization from sonar images, which contributes to an illustration.
Cardiovascular diseases and heart failures in particular are the main cause of non-communicable disease mortality in the world. Constant patient monitoring enables better medical treatment as it allows practitioners to react on time and provide the a ppropriate treatment. Telemedicine can provide constant remote monitoring so patients can stay in their homes, only requiring medical sensing equipment and network connections. A limiting factor for telemedical centers is the amount of patients that can be monitored simultaneously. We aim to increase this amount by implementing a decision support system. This paper investigates a machine learning model to estimate a risk score based on patient vital parameters that allows sorting all cases every day to help practitioners focus their limited capacities on the most severe cases. The model we propose reaches an AUCROC of 0.84, whereas the baseline rule-based model reaches an AUCROC of 0.73. Our results indicate that the usage of deep learning to improve the efficiency of telemedical centers is feasible. This way more patients could benefit from better health-care through remote monitoring.
Due to their promise of superior predictive power relative to human assessment, machine learning models are increasingly being used to support high-stakes decisions. However, the nature of the labels available for training these models often hampers the usefulness of predictive models for decision support. In this paper, we explore the use of historical expert decisions as a rich--yet imperfect--source of information, and we show that it can be leveraged to mitigate some of the limitations of learning from observed labels alone. We consider the problem of estimating expert consistency indirectly when each case in the data is assessed by a single expert, and propose influence functions based methodology as a solution to this problem. We then incorporate the estimated expert consistency into the predictive model meant for decision support through an approach we term label amalgamation. This allows the machine learning models to learn from experts in instances where there is expert consistency, and learn from the observed labels elsewhere. We show how the proposed approach can help mitigate common challenges of learning from observed labels alone, reducing the gap between the construct that the algorithm optimizes for and the construct of interest to experts. After providing intuition and theoretical results, we present empirical results in the context of child maltreatment hotline screenings. Here, we find that (1) there are high-risk cases whose risk is considered by the experts but not wholly captured in the target labels used to train a deployed model, and (2) the proposed approach improves recall for these cases.
Action selection from many options with few constraints is crucial for improvisation and co-creativity. Our previous work proposed creative arc negotiation to solve this problem, i.e., selecting actions to follow an author-defined `creative arc or tr ajectory over estimates of novelty, unexpectedness, and quality for potential actions. The CARNIVAL agent architecture demonstrated this approach for playing the Props game from improv theatre in the Robot Improv Circus installation. This article evaluates the creative arc negotiation experience with CARNIVAL through two crowdsourced observer studies and one improviser laboratory study. The studies focus on subjects ability to identify creative arcs in performance and their preference for creative arc negotiation compared to a random selection baseline. Our results show empirically that observers successfully identified creative arcs in performances. Both groups also preferred creative arc negotiation in agent creativity and logical coherence, while observers enjoyed it more too.

الأسئلة المقترحة

التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا