Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

PO2PO^2 - A Process and Observation Ontology in Food Science. Application to Dairy Gels

International audienceThis paper focuses on the knowledge representation task foran interdisciplinary project called Delicious concerning the productionand transformation processes in food science. The originality of thisproject is to combine data from different disciplines like food composition,food structure, sensorial perception and nutrition. Available datasets are described using different vocabularies and are stored in differentformats. Therefore there is a need to define an ontology, called PO2(Process and Observation Ontology), as a common and standardizedvocabulary for this project. The scenario 6 of the NeON methodologywas used for building PO2 and the core component is implemented inOWL. By making use of PO2, data from the project were structuredand an use case is presented here. PO2 aims to play a key role as therepresentation layer of the querying and simulation systems of Deliciousproject

Comp-O: an OWL-S Extension for Composite Service Description

International audienceComponent-based software engineering is a paradigm that fosters software flexibility and emphasizes composability and reuse of software components. These are runtime units that provide services and, in turn, may require other services to operate. Assembling components consists in binding compo-nents' required services to provided ones to deliver composite services with added value. Building a composite service is a challenging task as it requires identifying components and services that are compatible, binding them to implement the service, and describe it for discovery. For that, the vocabulary used to describe component-based services (i.e., services offered by components or assemblies) must support the description of required services, and descriptions must be combinable in order to automatically generate composite service descriptions. However, existing solutions are limited to the description and composition of provided (and not required) services. In this paper, we consider ontologies to describe component-based services implemented by component assemblies. After comparing existing service ontologies, we present an extension of OWLS called Comp-O. Through a proof-of-concept, we demonstrate how the added semantics can be handled to automatically build composite service descriptions