Computing abduction by using TMS with top-down expectation

AbstractWe present a method to compute abduction in logic programming. We translate an abductive framework into a normal logic program with integrity constraints and show the correspondence between generalized stable models and stable models for the translation of the abductive framework. Abductive explanations for an observation can be found from the stable models for the translated program by adding a special kind of integrity constraint for the observation. Then, we show a bottom-up procedure to compute stable models for a normal logic program with integrity constraints. The proposed procedure excludes the unnecessary construction of stable models on early stages of the procedure by checking integrity constraints during the construction and by deriving some facts from integrity constraints. Although a bottom-up procedure has the disadvantage of constructing stable models not related to an observation for computing abductive explanations in general, our procedure avoids the disadvantage by expecting which rule should be used for satisfaction of integrity constraints and starting bottom-up computation based on the expectation. This expectation is not only a technique to scope rule selection but also an indispensable part of our stable model construction because the expectation is done for dynamically generated constraints as well as the constraint for the observation

Optical Fiber-Based Sleep Apnea Syndrome Sensor

A noninvasive sleep apnea syndrome (SAS) sensor using optical fibers, the “F-SAS sensor,” has been evaluated in a clinical application ranging in age from 13 to 78 years and with BMIs of 19.2–39.3. The respiratory disturbance index (RDI) from the F-SAS sensor corresponded well with the apnea hypopnea index (AHI) from polysomnography (PSG). Concurrent measurement of the RDI and the AHI had a correlation coefficient of 0.71. This means that the F-SAS is well-suited for preliminary SAS screening. They would also be useful for screening potential SAS sufferers during normal sleep at home. Then, we have succeeded in downsizing F-SAS sensor and have recognized that it is highly correlated with PSG and pulse oximetry. Next, we applied the compact F-SAS sensor to examining SAS diagnosis in a child patient (2–12 years) and report on improved pediatric analysis. The analysis results revealed the correlation value to be R = 0.87 was a significant improvement over the correlation value of R = 0.697 between the AHI obtained by a sleep apnea syndrome examination apparatus (SAS 2100) and RDI obtained by the conventional F-SAS sensor

The role of ontologies and decision frameworks in computer-interpretable guideline execution

Computer-Interpretable Guidelines (CIGs) are machine readable representations of Clinical Practice Guidelines (CPGs) that serve as the knowledge base in many knowledge-based systems oriented towards clinical decision support. Herein we disclose a comprehensive CIG representation model based on Web Ontology Language (OWL) along with its main components. Additionally, we present results revealing the expressiveness of the model regarding a selected set of CPGs. The CIG model then serves as the basis of an architecture for an execution system that is able to manage incomplete information regarding the state of a patient through Speculative Computation. The architecture allows for the generation of clinical scenarios when there is missing information for clinical parameters.FCT - Fundação para a Ciência e a Tecnologia (SFRH/BD/85291/ 2012)info:eu-repo/semantics/publishedVersio