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![Fig. 2. The knowledge intensive-CBR cycle, taken from [12].](profile/Pariya-Kashfi/publication/278018427/figure/fig2/AS:361180750204932@1463123609225/The-knowledge-intensive-CBR-cycle-taken-from-12_Q320.jpg)
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Citations
... However the scalability of their approach and easy reusability of specialized archetypes have not been tested. Besides, Kashfi and Robledo found out that development of CDSS specific archetypes by clinicians may not actually be practical, possibly due to the fact that besides the information model, the knowledge and inference model needed to be taken into consideration [24]. ...
HL7 CDA, vMR, and openEHR archetypes have been utilized as standard information models for clinical decision support systems. Compared to openEHR archetypes, vMR typically requires less time to develop and extend which makes it a good fit for rapid prototyping and pilot projects, while openEHR archetypes handle the data and semantic specification better. Using CDA for clinical decision support systems is discouraged due to its complexity, steep learning curve, and potential safety issues.
... In fact, recent approaches on CDSS follow both reasoning types. The intuitive approach is followed by Case-Based Reasoning systems [11,21,27,74,2,1]. Their major limitation is that the quality of the output depends on the previous cases included in the knowledge base. ...
... For each new case inputed to the system, (i) first, the relevant pre-vious cases are retrieved; (ii) following, the knowledge in the cases retrieved is reused to propose a solution (classification) for the new one; (iii) the proposed solution is then revised by external means; and (iv) finally, the system learns by retaining the new case in the previous case base. The CBR allows some incremental learning, however the main limitation of current works on CBR [11,21,27,74,2,1] is their need to rely on static quantitative case characterizations in order to compute some kind of distance among cases to perform the search in the case database. The development of CDSS deals with imprecise and evolving characterizations of the decisional events that may not easily be dealt with by CBR. ...
Clinical Decision Support Systems (CDSS) are active knowledge resources that use patient data to generate case specific advice. The fast pace of change of clinical knowledge imposes to CDSS the continuous update of the domain knowledge and decision criteria. Traditional approaches require costly tedious manual maintenance of the CDSS knowledge bases and repositories. Often, such an effort cannot be assumed by medical teams, hence maintenance is often faulty. In this paper, we propose a (semi-)automatic update process of the underlying knowledge bases and decision criteria of CDSS, following a learning paradigm based on previous experiences, such as the continuous learning that clinicians carry out in real life. In this process clinical decisional events are acquired and formalized inside the system by the use of the SOEKS and Decisional DNA experiential knowledge representation techniques. We propose three algorithms processing clinical experience to: (a) provide a weighting of the different decision criteria, (b) obtain their fine-tuning, and (c) achieve the formalization of new decision criteria. Finally, we present an implementation instance of a CDSS for the domain of breast cancer diagnosis and treatment.
