Processing Metonymy: a Domain-Model Heuristic Graph Traversal Approach

We address here the treatment of metonymic expressions from a knowledge representation perspective, that is, in the context of a text understanding system which aims to build a conceptual representation from texts according to a domain model expressed in a knowledge representation formalism. We focus in this paper on the part of the semantic analyser which deals with semantic composition. We explain how we use the domain model to handle metonymy dynamically, and more generally, to underlie semantic composition, using the knowledge descriptions attached to each concept of our ontology as a kind of concept-level, multiple-role qualia structure. We rely for this on a heuristic path search algorithm that exploits the graphic aspects of the conceptual graphs formalism. The methods described have been implemented and applied on French texts in the medical domain.Comment: 6 pages, LaTeX, one encapsulated PostScript figure, uses colap.sty (included) and epsf.sty (available from the cmp-lg macro library). To appear in Coling-9

Profils patients associés à la non conformité des décisions aux recommandations de prise en charge thérapeutique des cancers du sein : utilisation de l'analyse de concepts formels

International audienceLes systèmes d'aide à la décision médicale permettent d'améliorer le suivi des recommandations de pratique clinique. OncoDoc2 est un tel système s’appuyant sur des recommandations de prise en charge du cancer du sein. Malgré son utilisation en routine lors de réunions de concertation pluridisciplinaire de sénologie, des décisions non conformes aux recommandations subsistent. L'objectif est d'utiliser l'analyse de concepts formels afin de caractériser les profils patients associés aux deux modalités de la conformité. Deux étapes de pré-traitement permettant de simplifier les données à analyser sont proposées : une réduction d'attributs par suppression de ceux non statistiquement associés à la non conformité, et un gommage sélectif de valeurs. Parmi les décisions recueillies sur 3 ans à l'hôpital Tenon, 198 concernent la reprise chirurgicale et ont été analysées. Les profils patients associés à la non conformité retrouvés sont ceux pour lesquels il n'existe pas de preuve scientifique des recommandations. Mots-clés

Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach

International audienceCase-Based Reasoning (CBR) is a form of analogical reasoning in which the solution for a (new) query case is determined using a database of previous known cases with their solutions. Cases similar to the query are retrieved from the database, and then their solutions are adapted to the query. In medicine, a case usually corresponds to a patient and the problem consists of classifying the patient in a class of diagnostic or therapy. Compared to "black box" algorithms such as deep learning, the responses of CBR systems can be justified easily using the similar cases as examples. However, this possibility is often under-exploited and the explanations provided by most CBR systems are limited to the display of the similar cases. In this paper, we propose a CBR method that can be both executed automatically as an algorithm and presented visually in a user interface for providing visual explanations or for visual reasoning. After retrieving similar cases, a visual interface displays quantitative and qualitative similarities between the query and the similar cases, so as one can easily classify the query through visual reasoning, in a fully explainable manner. It combines a quantitative approach (visualized by a scatter plot based on Multidimensional Scaling in polar coordinates , preserving distances involving the query) and a qualitative approach (set visualization using rainbow boxes). We applied this method to breast cancer management. We showed on three public datasets that our qualitative method has a classification accuracy comparable to k-Nearest Neighbors algorithms, but is better explainable. We also tested the proposed interface during a small user study. Finally, we apply the proposed approach to a real dataset in breast cancer. Medical experts found the visual approach interesting as it explains why cases are similar through the visualization of shared patient characteristics

Implementing Guideline-based, Experience-based, and Case-based approaches to enrich decision support for the management of breast cancer patients in the DESIREE project

DESIREE is a European-funded project to improve the management of primary breast cancer. We have developed three decision support systems (DSSs), a guideline-based, an experience-based, and a case-based DSSs, resp. GL-DSS, EXP-DSS, and CB-DSS, that operate simultaneously to offer an enriched multi-modal decision support to clinicians. A breast cancer knowledge model has been built to describe within a common ontology the data model and the termino-ontological knowledge used for representing breast cancer patient cases. It allows for rule-based and subsumption-based reasoning in the GL-DSS to provide best patient-centered reconciled care plans. It also allows for using semantic similarity in the retrieval algorithm implemented in the CB-DSS. Rainbow boxes are used to display patient cases similar to a given query patient. This innovative visualization technique translates the question of deciding the most appropriate treatment into a question of deciding the colour dominance among boxes

Reconciliation of Multiple Guidelines for Decision Support: A case study on the multidisciplinary management of breast cancer within the DESIREE project

Breast cancer is the most common cancer among women. DESIREE is a European project which aims at developing web-based services for the management of primary breast cancer by multidisciplinary breast units (BUs). We describe the guideline-based decision support system (GL-DSS) of the project. Various breast cancer clinical practice guidelines (CPGs) have been selected to be concurrently applied to provide state-of-the-art patient-specific recommendations. The aim is to reconcile CPG recommendations with the objective of complementarity to enlarge the number of clinical situations covered by the GL-DSS. Input and output data exchange with the GL-DSS is performed using FHIR. We used a knowledge model of the domain as an ontology on which relies the reasoning process performed by rules that encode the selected CPGs. Semantic web tools were used, notably the Euler/EYE inference engine, to implement the GL-DSS. "Rainbow boxes" are a synthetic tabular display used to visualize the inferred recommendations

Case-based decision support system for breast cancer management

Breast cancer is identified as the most common type of cancer in women worldwide with 1.6 million women around the world diagnosed every year. This prompts many active areas of research in identifying better ways to prevent, detect, and treat breast cancer. DESIREE is a European Union funded project, which aims at developing a web-based software ecosystem for the multidisciplinary management of primary breast cancer. The development of an intelligent clinical decision support system offering various modalities of decision support is one of the key objectives of the project. This paper explores case-based reasoning as a problem solving paradigm and discusses the use of an explicit domain knowledge ontology in the development of a knowledge-intensive case-based decision support system for breast cancer management

Understanding metonymies in discourse

We propose a new computational model for the resolution of metonymies, a particular type of figurative language. Typically, metonymies are considered as a violation of semantic constraints (e.g., those expressed by selectional restrictions) that require some repair mechanism (e.g., type coercion) for proper interpretation. We reject this view, arguing that it misses out on the interpretation of a considerable number of utterances. Instead, we treat literal and figurative language on a par, by computing both kinds of interpretation independently from each other as long as their semantic representation structures are consistent with the underlying knowledge representation structures of the domain of discourse. The following general heuristic principles apply for making reasonable selections from the emerging readings. We argue that the embedding of utterances in a coherent discourse context is as important for recognizing and interpreting metonymic utterances as intrasentential semantic constraints. Therefore, in our approach, (metonymic or literal) interpretations that establish referential cohesion are preferred over ones that do not. In addition, metonymic interpretations that conform to a metonymy schema are preferred over metonymic ones that do not, and metonymic interpretations that are in conformance with knowledge-based aptness conditions are preferred over metonymic ones that are not. We lend further credit to our model by discussing empirical data from an evaluation study which highlights the importance of the discourse embedding of metonymy interpretation for both anaphora and metonymy resolution

Quels sont les patients atteints d'un cancer du sein dont la décision de prise en charge thérapeutique bénéficie de l'utilisation d'un système d'aide à la décision ? Un exemple utilisant la fouille de données et OncoDoc2

Session 2 : Utilisateurs et usagesNational audienceOncoDoc2 est un système d'aide à la décision (SAD) s'appuyant sur des recommandations de pratique clinique (RPC) pour la prise en charge des cancers du sein. Il a été utilisé comme intervention dans un essai randomisé contrôlé dont l'objectif principal était d'évaluer son impact sur la conformité des décisions des réunions de concertation pluridisciplinaire aux RPC. Nous avons utilisé un algorithme de fouille de données pour découvrir les régularités des profils patients, ou " motifs émergents " (ME), associées à la conformité et à la non-conformité des décisions selon que le système OncoDoc2 était ou non utilisé, afin d'évaluer quels profils patients pouvaient bénéficier de l'utilisation du système. Les ME associés à la non conformité des décisions prises sans le système sont associées à la conformité quand le système est utilisé sauf dans certaines situations cliniques pour lesquelles la force de la recommandation est faible