Case-based reasoning for context-aware solutions supporting personalised asthma management

Context-aware solutions have the potential to address the personalisation required for implementing asthma management plans. However, they have limitations to aid people with asthma when their triggers and symptoms are poorly known or changing. Case-Based Reasoning can address these limitations as it can effectively deal with personal constraints in problems that involve evolving context adaptation. This research work proposes to use Case-Based Reasoning together with Context-Aware Reasoning to aid the personalisation of asthma management plans at specific stages of the condition when the triggers and symptoms are not completely known or evolving. The proposal was implemented and evaluated using historical weather and air pollution data and two control cases that were defined based on a set of interviews. Finally, the benefits and challenges of the proposal are presented and analysed based on the results of the evaluation

A Human-in-The-Loop context-aware system allowing the application of case-based reasoning for asthma management

Determining the asthma health status of a person is a relevant task in the application of context-awareness and case-based reasoning for asthma management. As there are no devices that can track the asthma health status of a person constantly, it is necessary to use a Human-in-The-Loop (HiTL) approach for creating a solution able to associate their health status with context-related data. This research work proposes a system that implements the Asthma Control Questionnaire (ACQ) for determining the asthma health status of a person. The system links this health status to context-related data the person is exposed and creates the cases to be used by the CBR component of the system. The system is then evaluated by users from a usability perspective through the Health IT Usability Evaluation Model (Health-ITUEM)

Exploring and linking biomedical resources through multidimensional semantic spaces

Background The semantic integration of biomedical resources is still a challenging issue which is required for effective information processing and data analysis. The availability of comprehensive knowledge resources such as biomedical ontologies and integrated thesauri greatly facilitates this integration effort by means of semantic annotation, which allows disparate data formats and contents to be expressed under a common semantic space. In this paper, we propose a multidimensional representation for such a semantic space, where dimensions regard the different perspectives in biomedical research (e.g., population, disease, anatomy and protein/genes). Results This paper presents a novel method for building multidimensional semantic spaces from semantically annotated biomedical data collections. This method consists of two main processes: knowledge and data normalization. The former one arranges the concepts provided by a reference knowledge resource (e.g., biomedical ontologies and thesauri) into a set of hierarchical dimensions for analysis purposes. The latter one reduces the annotation set associated to each collection item into a set of points of the multidimensional space. Additionally, we have developed a visual tool, called 3D-Browser, which implements OLAP-like operators over the generated multidimensional space. The method and the tool have been tested and evaluated in the context of the Health-e-Child (HeC) project. Automatic semantic annotation was applied to tag three collections of abstracts taken from PubMed, one for each target disease of the project, the Uniprot database, and the HeC patient record database. We adopted the UMLS Meta-thesaurus 2010AA as the reference knowledge resource. Conclusions Current knowledge resources and semantic-aware technology make possible the integration of biomedical resources. Such an integration is performed through semantic annotation of the intended biomedical data resources. This paper shows how these annotations can be exploited for integration, exploration, and analysis tasks. Results over a real scenario demonstrate the viability and usefulness of the approach, as well as the quality of the generated multidimensional semantic spaces

A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment

Cancer is one of the most common diseases worldwide and its treatment is a complex and time-consuming process. Specifically, prostate cancer as the most common cancer among male population has received the attentions of many researchers. Oncologists and medical physicists usually rely on their past experience and expertise to prescribe the dose plan for cancer treatment. The main objective of dose planning process is to deliver high dose to the cancerous cells and simultaneously minimize the side effects of the treatment. In this article, a novel TOPSIS case based reasoning goal-programming approach has been proposed to optimize the dose plan for prostate cancer treatment. Firstly, a hybrid retrieval process TOPSIS–CBR [technique for order preference by similarity to ideal solution (TOPSIS) and case based reasoning (CBR)] is used to capture the expertise and experience of oncologists. Thereafter, the dose plans of retrieved cases are adjusted using goal-programming mathematical model. This approach will not only help oncologists to make a better trade-off between different conflicting decision making criteria but will also deliver a high dose to the cancerous cells with minimal and necessary effect on surrounding organs at risk. The efficacy of proposed method is tested on a real data set collected from Nottingham City Hospital using leave-one-out strategy. In most of the cases treatment plans generated by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better. Developed decision support system can assist both new and experienced oncologists in the treatment planning process

Case Based Representation and Retrieval with Time Dependent Features

Abstract. The temporal dimension of the knowledge embedded in cases has often been neglected or oversimplified in Case Based Reasoning sys-tems. However, in several real world problems a case should capture the evolution of the observed phenomenon over time. To this end, we propose to represent temporal information at two levels: (1) at the case level, if some features describe parameters varying within a period of time (which corresponds to the case duration), and are therefore collected in the form of time series; (2) at the history level, if the evolution of the system can be reconstructed by retrieving temporally related cases. In this paper, we describe a framework for case representation and retrieval able to take into account the temporal dimension, and meant to be used in any time dependent domain. In particular, to support case retrieval, we provide an analysis of similarity-based time series retrieval techniques; to support history retrieval, we introduce possible ways to summarize the case content, together with the corresponding strategies for identifying similar instances in the knowledge base. A concrete ap-plication of our framework is represented by the system RHENE, which is briefly sketched here, and extensively described in [20].

Report on the Eighteenth International Conference on Case-Based Reasoning

This article reports on the main track papers, speakers, satellite events, and other activities of the Eighteenth International Conference on Case-Based Reasoning (ICCBR), held 19-22 July 2010 in Alessandria, Ital