eXiT*CBR: A framework for case-based medical diagnosis development and experimentation

López, Beatriz; Pous, Carles; Plà, Albert; Sanz, J; Brunet, Joan

doi:10.1016/j.artmed.2010.09.002

Cited by 32 publications

(16 citation statements)

References 31 publications

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“…The Compaq SMART system was developed and applied using CBR to support the Compaq help desk [27]. Particularly relevant to this study is the fact that many CBR systems have been applied in medical decision making, such as CBR for medical knowledge-based systems [28], CBR in the health sciences [29], CBR for the prognosis and diagnosis of chronic diseases [30], case-based medical diagnosis, development, and experimentation [31], and a distributed CBR tool for medical prognosis [32]. Recently, CBR has been applied in problem-solving complex domains, including planning [33], law [34], e-learning [35], knowledge management [36], image processing [37], and recommender systems [38].…”

Section: Related Workmentioning

confidence: 99%

When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning

Kwon

Kim

Lee

et al. 2018

Journal of Healthcare Engineering

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One of the significant issues in a smart city is maintaining a healthy environment. To improve the environment, huge amounts of data are gathered, manipulated, analyzed, and utilized, and these data might include noise, uncertainty, or unexpected mistreatment of the data. In some datasets, the class imbalance problem skews the learning performance of the classification algorithms. In this paper, we propose a case-based reasoning method that combines the use of crowd knowledge from open source data and collective knowledge. This method mitigates the class imbalance issues resulting from datasets, which diagnose wellness levels in patients suffering from stress or depression. We investigate effective ways to mitigate class imbalance issues in which the datasets have a higher proportion of one class over another. The results of this proposed hybrid reasoning method, using a combination of crowd knowledge extracted from open source data (i.e., a Google search, or other publicly accessible source) and collective knowledge (i.e., case-based reasoning), were that it performs better than other traditional methods (e.g., SMO, BayesNet, IBk, Logistic, C4.5, and crowd reasoning). We also demonstrate that the use of open source and big data improves the classification performance when used in addition to conventional classification algorithms.

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Section: Related Workmentioning

confidence: 99%

When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning

Kwon

Kim

Lee

et al. 2018

Journal of Healthcare Engineering

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“…It does this by providing a user-friendly navigation interface which facilitates the analysis of experiments. It is adapted to the information physicians use to work (receiver operator curves, ROC), and supports experiment reproducibility by storing and tracking experiment information [5]. In this paper we discuss new features of the v2 tool that complement the cooperative nature of medical teams.…”

Section: Distributed Case-based Reasoningmentioning

confidence: 99%

“…Hybridisation with other reasoning methods as well as with data mining tools is also possible. See [5] for further details.…”

Section: Exit*cbr Basicsmentioning

confidence: 99%

“…Based exclusively on CBR, it has more flexibility for defining similarity mea-sures, and includes a semantic module. For a detailed comparison of other tools against the non-agent version of eXiT*CBR, see [5].…”

Section: Comparison With Similar Toolsmentioning

confidence: 99%

“…Technique oriented tools support decision making according to the underlying theory, but the development of domain oriented tools brings the advantage of providing an adequate interface to their targeted users, offering added value as well as enhancing technology acceptance [3]. In the medical field, several particularities have been identified in [4], and we have addressed some of them with the development of a medically specific case-based reasoning tool called eXiT*CBR [5]. In particular, we provide support and interpretation of results in medical metrics, experiment reproducibility and heterogeneous data management.…”

Section: Introductionmentioning

confidence: 99%

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eXiT*CBR.v2: Distributed case-based reasoning tool for medical prognosis

Plà

López

Gay

et al. 2013

Decision Support Systems

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In this work we propose a user-friendly medically oriented tool for prognosis development systems and experimentation under a case-based reasoning methodology. The tool enables health care collaboration practice to be mapped in cases where different doctors share their expertise, for example, or where medical committee composed of specialists from different fields work together to achieve a final prognosis. Each agent with a different piece of knowledge classifies the given cases through metrics designed for this purpose. Since multiple solutions for the same case is useless, agents collaborate among themselves in order to achieve a final decision through a coordinated schema. For this purpose, the tool provides a weighted voting schema and an evolutionary algorithm (genetic algorithm) to learn robust weights. Moreover, to test the experiments, the tool includes stratified cross-validation methods which take the collaborative environment into account. In this paper the different collaborative facilities offered by the tool are described. A sample usage of the tool is also provided.

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