CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions

Building-up urban scenarios: assessing institutional feasibility and political viability of strategic trajectories

The paper discusses a simple methodological tool for supporting the participatory elaboration of future urban scenarios through successive approximations. It systematizes one possible way to build answers to a set of critical questions when attempting to compose strategic urban programs. To this end, we focused on devising a simple sequence of understandable, easy-to-apply Multi Criteria Evaluation instruments which - by means of ‘light’ ‘accounting’ calculation procedures - may “help (planners) to think” (Calcagno, 1972). The deliberate simplicity of this approach - an ‘intelligent’, transparent and intentional management of elements of analytical and intervention fields, funded on their exhaustive qualitative descriptions and on explicit statements of interests and purposes - prioritises the political side of decision – making. Thus, the paper also seeks to contribute to the reflection on the roles and views of social actors when defining and constructing urban public scenarios. First, we discuss the conceptual and political contents and implications of Diagnoses and Scenario- building, in which involved actors (i) elaborate causal interpretations of urban processes, (ii) specify and select strategic trajectories and (iii) compose and structure project portfolios, by (eventually) supporting them through progressive agreements and consensus. Next, we propose a methodological approach to assessing institutional feasibility and political viability of given strategic trajectories. Finally, we discuss some contextual and operational conditions of these analyses. On the technical side, this framework is originally focused on the interactions among Land Use, Mobility and Energy consumption patterns, three strong determinants of the socio-spatial structuring of territories which - both in Argentina and other Latin American countries - are seldom addressed through transversal, integrated planning approaches. Quite on the contrary, public urban management models – referred to those as well as to other relevant drivers of urban structuring - are most often characterized by remarkable jurisdictional and institutional disarticulation, high technical and thematic fragmentation, ritual emphases on bureaucratic processes rather than on factual objectives or results. Accordingly, formulation and evaluation of public urban policies usually present extremely low levels of systemic completeness. It is suggested that - in these types of highly fragmented governance environments - this approach may contribute to making experts’ and social actors’ views explicit and to enabling transversal thinking. The analysis of diverse structuring, feasibility and viability assessments may – if applied through successive approximations - help planners to reconfigure the strategic composition of public policies, whether by (i) modifying the strategy portfolio, (ii) modifying the sequence of strategies or (iii)incorporating new strategies in order to modify the balance between involved actors’ rejections and supports, while maintaining the meanings, orientations and aims of the evaluated policy.Facultad de Arquitectura y Urbanism

Building-up urban scenarios: assessing institutional feasibility and political viability of strategic trajectories

The paper discusses a simple methodological tool for supporting the participatory elaboration of future urban scenarios through successive approximations. It systematizes one possible way to build answers to a set of critical questions when attempting to compose strategic urban programs. To this end, we focused on devising a simple sequence of understandable, easy-to-apply Multi Criteria Evaluation instruments which - by means of ‘light’ ‘accounting’ calculation procedures - may “help (planners) to think” (Calcagno, 1972). The deliberate simplicity of this approach - an ‘intelligent’, transparent and intentional management of elements of analytical and intervention fields, funded on their exhaustive qualitative descriptions and on explicit statements of interests and purposes - prioritises the political side of decision – making. Thus, the paper also seeks to contribute to the reflection on the roles and views of social actors when defining and constructing urban public scenarios. First, we discuss the conceptual and political contents and implications of Diagnoses and Scenario- building, in which involved actors (i) elaborate causal interpretations of urban processes, (ii) specify and select strategic trajectories and (iii) compose and structure project portfolios, by (eventually) supporting them through progressive agreements and consensus. Next, we propose a methodological approach to assessing institutional feasibility and political viability of given strategic trajectories. Finally, we discuss some contextual and operational conditions of these analyses. On the technical side, this framework is originally focused on the interactions among Land Use, Mobility and Energy consumption patterns, three strong determinants of the socio-spatial structuring of territories which - both in Argentina and other Latin American countries - are seldom addressed through transversal, integrated planning approaches. Quite on the contrary, public urban management models – referred to those as well as to other relevant drivers of urban structuring - are most often characterized by remarkable jurisdictional and institutional disarticulation, high technical and thematic fragmentation, ritual emphases on bureaucratic processes rather than on factual objectives or results. Accordingly, formulation and evaluation of public urban policies usually present extremely low levels of systemic completeness. It is suggested that - in these types of highly fragmented governance environments - this approach may contribute to making experts’ and social actors’ views explicit and to enabling transversal thinking. The analysis of diverse structuring, feasibility and viability assessments may – if applied through successive approximations - help planners to reconfigure the strategic composition of public policies, whether by (i) modifying the strategy portfolio, (ii) modifying the sequence of strategies or (iii)incorporating new strategies in order to modify the balance between involved actors’ rejections and supports, while maintaining the meanings, orientations and aims of the evaluated policy.Facultad de Arquitectura y Urbanism

Object-driven and temporal action rules mining

In this thesis, I present my complete research work in the field of action rules, more precisely object-driven and temporal action rules. The drive behind the introduction of object-driven and temporally based action rules is to bring forth an adapted ap- proach to extract action rules from a subclass of systems that have a specific nature, in which instances are observed from assumingly different distributions (defined by an object attribute), and in which each instance is coupled with a time-stamp. In previous publications, we proposed an object-independency assumption that suggests extracting patterns from subsystems defined by unique objects, and then aggregat- ing similar patterns amongst all objects. The motivation behind this approach is based on the fact that same-object observations share similar features that are not shared with other objects, and these features are possibly not explicitly included in our dataset. Therefore, by individualizing objects prior to calculating action rules, variance is reduced, and over-fitting is potentially avoided. In addition to the object- independency assumption, temporal information is exploited by taking into account only the state transitions that occurred in the valid direction.j The common nature of object-driven and temporal action rules made us believe that this work is general enough to solve a diverse fields of areas where it is highly needed. In our case study, we show how our approach was applied to an information system of hypernasality patients; our results were further investigated by physicians collaborators to confirm them

Improving performance through concept formation and conceptual clustering

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes

Instance-based learning following physician reasoning for assistance during medical consultation

Esta tesis de maestría presenta un sistema automático que modela el conocimiento clínico para seguir el razonamiento médico durante una consulta ambulatoria. Se aplica un método de aprendizaje basado en instancias para proporcionar sugerencias durante el registro en una historia clínica electrónica. El método de aprendizaje propuesto tiene en cuenta la base de conocimiento clínico de cada médico, para presentar sugerencias basadas en tipos de casos clínicos previamente definidos, y deducidos según una métrica de similitud específicamente diseñada. El sistema se valida en un escenario de uso real, con la participación de estudiantes avanzados de medicina de un curso de informática médica de la Universidad de la República, Uruguay. Los resultados demuestran que el sistema propuesto es 2.5x más eficiente que una herramienta empírica de referencia para sugerencias, y dos órdenes de magnitud más rápido que un método de aprendizaje Bayesiano, considerando un marco de referencia de 250 tipos de casos clínicos. Los resultados también demuestran que el método de aprendizaje es capaz de producir sugerencias en tiempos razonables, incluso cuando se procesan grandes volúmenes de datos. Una encuesta realizada a estudiantes avanzados de medicina destaca que el enfoque propuesto se considera apropiado para la práctica médica. Esta investigación introduce una estructura formal para representar con precisión el conocimiento clínico, que apoya a los principales flujos que ocurren durante las consultas médicas. También se proporciona un marco que permite implementar un sistema en tiempo real capaz de asistir a los médicos durante sus consultas, y que además ayuda a reducir el tiempo de registro.This Master Thesis presents an automatic system for modeling clinical knowledge to follow physicians reasoning in medical consultation. Instance-based learning is applied to provide suggestions when recording electronicmedical records. The proposed learning method takes into account the clinical knowledge base of a physician, in order to present suggestions based on previously-defined clinical case types, and deduced according to an ad-hoc similarity metric. The system is validated on a real case study involving advanced medical students of a Medical Informatics course at Universidad de la República, Uruguay. Results show that the proposed system is 2.5× more efficient than a base-line empirical tool for suggestions, and two orders of magnitude faster than a Bayesian learning method, when processing a testbed of 250 clinical casetypes. Results also demostrate that the learning method is able to produce suggestions in a reasonable time frame, even when processing large volumes of data. A survey performed on advanced medical students highlights that the proposed approach is considered appropriate for medical practices. The research introduces a formal structure to accurately represent clinical knowledge, supporting the main flows of medical consultations. A frame for implementing a real-time system for assisting physicians during medical consultations is also provided, which helps reducing the time needed to register medical consultations

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions