Hybrid case‑base maintenance approach for modeling large scale case‑based reasoning systems

YesCase-based reasoning (CBR) is a nature inspired paradigm of machine learning capable to continuously learn from the past experience. Each newly solved problem and its corresponding solution is retained in its central knowledge repository called case-base. Withρ the regular use of the CBR system, the case-base cardinality keeps on growing. It results into performance bottleneck as the number of comparisons of each new problem with the existing problems also increases with the case-base growth. To address this performance bottleneck, different case-base maintenance (CBM) strategies are used so that the growth of the case-base is controlled without compromising on the utility of knowledge maintained in the case-base. This research work presents a hybrid case-base maintenance approach which equally utilizes the benefits of case addition as well as case deletion strategies to maintain the case-base in online and offline modes respectively. The proposed maintenance method has been evaluated using a simulated model of autonomic forest fire application and its performance has been compared with the existing approaches on a large case-base of the simulated case study.Authors acknowledge the internal funding support received from Namal College Mianwali to complete the research work

Study of similarity measures for case-based reasoning in transcatheter aortic valve implantation

International audienceCase-Based Reasoning (CBR) uses previous experiences to solve similar current problems. The basic hypothesis is that similar cases should have similar solutions. In the case of Transcatheter Aortic Valve Implantation (TAVI), the CBR could help practitioners to plan the procedure. Four steps compose a CBR retrieve, reuse, revise and retain. Defining a convenient similarity measure (SM) is essential in the retrieve step. This study aims to analyze the performance of different similarity measures and attribute selections. Generally in the retrieve step, a standard weighted heterogeneous similarity measure (WHSM) is used, in association with the k-nearest neighbor algorithm. Based on WHSM, we considered new definitions of SMs dedicated to decision support for TAVI. They include attributes selection and weight determination through a clinical decision tree. The performance of SMs was evaluated on a set of 100 cases with a leave-one-out cross validation. Results show that the CBR retrieving process can be improved by using dedicated SMs. © 2017 IEEE Computer Society. All rights reserved

Computational Modelling in the Management of Patients with Aortic Valve Stenosis

Background Stenosis of the aortic valve causes increased left ventricular pressure leading to adverse clinical outcomes. The selection and timing of intervention (surgical replacement or transcatheter implantation) is often unclear and is based upon limited data. Hypothesis A comprehensive and integrated personalised approach, including recognition of cardiac energetics parameters extracted from a personalised mathematical model, mapped to patient activity, has the potential to improve diagnosis and the planning and timing of interventions. Aims This project seeks to implement a simple, personalised, mathematical model of patients with aortic stenosis (AS), which can ‘measure’ cardiac work and power parameters that provide an effective characterisation of the demand on the heart in both rest and exercise conditions and can predict the changes of these parameters following an intervention. The specific aims of this project are: • to critically review current diagnostic methods • to evaluate the potential role of pre- and post-procedural measured patient activity • to implement a simple, personalised, mathematical model of patients with AS • to evaluate the potential role of a clinical decision support system Methods Twenty-two patients with severe AS according to ESC criteria were recruited. Relevant clinical, imaging, activity monitoring, six-minute walk test, and patient reported data were collected, before and early and after treatment. Novel imaging techniques were developed to help in the diagnosis of AS. A computational model was developed and executed using the data collected to create non-invasive pressure volume loops and study the global haemodynamic burden on the left ventricle. Simulations were run to predict the haemodynamic parameters both during exercise and following intervention. Modelled parameters were validated against clinically measured values. This information was then correlated with symptoms and activity data. A clinical decision support tool was created and populated with data obtained and its clinical utility evaluated. Outcomes The results of this project suggest that the combination of imaging and activity data with computational modelling provides a novel, patient-specific insight into patients’ haemodynamics and may help guide clinical decision making in patients with AS