An argument-based approach to reasoning with clinical knowledge

Better use of biomedical knowledge is an increasingly pressing concern for tackling challenging diseases and for generally improving the quality of healthcare. The quantity of biomedical knowledge is enormous and it is rapidly increasing. Furthermore, in many areas it is incomplete and inconsistent. The development of techniques for representing and reasoning with biomedical knowledge is therefore a timely and potentially valuable goal. In this paper, we focus on an important and common type of biomedical knowledge that has been obtained from clinical trials and studies. We aim for (1) a simple language for representing the results of clinical trials and studies; (2) transparent reasoning with that knowledge that is intuitive and understandable to users; and (3) simple computation mechanisms with this knowledge in order to facilitate the development of viable implementations. Our approach is to propose a logical language that is tailored to the needs of representing and reasoning with the results of clinical trials and studies. Using this logical language, we generate arguments and counterarguments for the relative merits of treatments. In this way, the incompleteness and inconsistency in the knowledge is analysed via argumentation. In addition to motivating and formalising the logical and argumentation aspects of the framework, we provide algorithms and computational complexity results

An argument-based approach to reasoning with clinical knowledge

Better use of biomedical knowledge is an increasingly pressing concern for tackling challenging diseases and for generally improving the quality of healthcare. The quantity of biomedical knowledge is enormous and it is rapidly increasing. Furthermore, in many areas it is incomplete and inconsistent. The development of techniques for representing and reasoning with biomedical knowledge is therefore a timely and potentially valuable goal. In this paper, we focus on an important and common type of biomedical knowledge that has been obtained from clinical trials and studies. We aim for (1) a simple language for representing the results of clinical trials and studies; (2) transparent reasoning with that knowledge that is intuitive and understandable to users; and (3) simple computation mechanisms with this knowledge in order to facilitate the development of viable implementations. Our approach is to propose a logical language that is tailored to the needs of representing and reasoning with the results of clinical trials and studies. Using this logical language, we generate arguments and counterarguments for the relative merits of treatments. In this way, the incompleteness and inconsistency in the knowledge is analysed via argumentation. In addition to motivating and formalising the logical and argumentation aspects of the framework, we provide algorithms and computational complexity results

Argue to agree: A case-based argumentation approach

[EN] The capability of reaching agreements is a necessary feature that large computer systems where agents interoperate must include. In these systems, agents represent self-motivated entities that have a social context, including dependency relations among them, and different preferences and beliefs. Without agreement there is no cooperation and thus, complex tasks which require the interaction of agents with different points of view cannot be performed. In this work, we propose a case-based argumentation approach for Multi-Agent Systems where agents reach agreements by arguing and improve their argumentation skills from experience. A set of knowledge resources and a reasoning process that agents can use to manage their positions and arguments are presented. These elements are implemented and validated in a customer support application.This work is supported by the Spanish government grants [CONSOLIDER-INGENIO 2010 CSD2007-00022, TIN2008-04446, and TIN2009-13839-C03-01] and by the GVA project [PROMETEO 2008/051].Heras Barberá, SM.; Jordán Prunera, JM.; Botti, V.; Julian Inglada, VJ. (2013). Argue to agree: A case-based argumentation approach. International Journal of Approximate Reasoning. 54(1):82-108. https://doi.org/10.1016/j.ijar.2012.06.005S8210854

Argumentation Schemes for Clinical Decision Support

This paper demonstrates how argumentation schemes can be used in decision support systems that help clinicians in making treatment decisions. The work builds on the use of computational argumentation, a rigorous approach to reasoning with complex data that places strong emphasis on being able to justify and explain the decisions that are recommended. The main contribution of the paper is to present a novel set of specialised argumentation schemes that can be used in the context of a clinical decision support system to assist in reasoning about what treatments to offer. These schemes provide a mechanism for capturing clinical reasoning in such a way that it can be handled by the formal reasoning mechanisms of formal argumentation. The paper describes how the integration between argumentation schemes and formal argumentation may be carried out, sketches how this is achieved by an implementation that we have created, and illustrates the overall process on a small set of case studies

Algorithms for computational argumentation in artificial intelligence

Argumentation is a vital aspect of intelligent behaviour by humans. It provides the means for comparing information by analysing pros and cons when trying to make a decision. Formalising argumentation in computational environment has become a topic of increasing interest in artificial intelligence research over the last decade. Computational argumentation involves reasoning with uncertainty by making use of logic in order to formalize the presentation of arguments and counterarguments and deal with conflicting information. A common assumption for logic-based argumentation is that an argument is a pair where Φ is a consistent set which is minimal for entailing a claim α. Different logics provide different definitions for consistency and entailment and hence give different options for formalising arguments and counterarguments. The expressivity of classical propositional logic allows for complicated knowledge to be represented but its computational cost is an issue. This thesis is based on monological argumentation using classical propositional logic [12] and aims in developing algorithms that are viable despite the computational cost. The proposed solution adapts well established techniques for automated theorem proving, based on resolution and connection graphs. A connection graph is a graph where each node is a clause and each arc denotes there exist complementary disjuncts between nodes. A connection graph allows for a substantially reduced search space to be used when seeking all the arguments for a claim from a given knowledgebase. In addition, its structure provides information on how its nodes can be linked with each other by resolution, providing this way the basis for applying algorithms which search for arguments by traversing the graph. The correctness of this approach is supported by theoretical results, while experimental evaluation demonstrates the viability of the algorithms developed. In addition, an extension of the theoretical work for propositional logic to first-order logic is introduced