title: A customisable framework for the assessment of therapies in the solution of therapy decision tasks.
abstract: In current medical research, a growing interest can be observed in the definition of a global therapy-evaluation framework which integrates considerations such as patients preferences and quality-of-life results. In this article, we propose the use of the research results in this domain as a source of knowledge in the design of support systems for therapy decision analysis, in particular with a view to application in oncology. We discuss the incorporation of these considerations in the definition of the therapy-assessment methods involved in the solution of a generic therapy decision task, described in the context of AI software development methodologies such as CommonKADS. The goal of the therapy decision task is to identify the ideal therapy, for a given patient, in accordance with a set of objectives of a diverse nature. The assessment methods applied are based either on data obtained from statistics or on the specific idiosyncrasies of each patient, as identified from their responses to a suite of psychological tests. In the analysis of the therapy decision task we emphasise the importance, from a methodological perspective, of using a rigorous approach to the modelling of domain ontologies and domain-specific data. To this aim we make extensive use of the semi-formal object oriented analysis notation UML to describe the domain level.
authors.affiliation: Departamento Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), Senda del Rey s/n, 28040, Madrid, Spain. jmira@dia.uned.es
authors.forename: A
authors.initials: A
authors.lastname: Manjarrés Riesco
authors.forename: R
authors.initials: R
authors.lastname: Martínez Tomás
authors.forename: J
authors.initials: J
authors.lastname: Mira Mira
mesh_terms: D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D006801:Humans; D011381:Programming Languages; D012984:Software; D013813:Therapy, Computer-Assisted