Abstract
In a clinical decision support system, the purpose of case-based reasoning is to help clinicians make convenient decisions for diagnoses or interventional gestures. Past experience, which is represented by a case-base of previous patients, is exploited to solve similar current problems using four steps—retrieve, reuse, revise, and retain. The proposed case-based reasoning has been focused on transcatheter aortic valve implantation to respond to clinical issues pertaining vascular access and prosthesis choices. The computation of a relevant similarity measure is an essential processing step employed to obtain a set of retrieved cases from a case-base. A hierarchical similarity measure that is based on a clinical decision tree is proposed to better integrate the clinical knowledge, especially in terms of case representation, case selection and attributes weighting. A case-base of 138 patients is used to evaluate the case-based reasoning performance, and retrieve- and reuse-based criteria have been considered. The sensitivity for the vascular access and the prosthesis choice is found to 0.88 and 0.94, respectively, with the use of the hierarchical similarity measure as opposed to 0.53 and 0.79 for the standard similarity measure. Ninety percent of the suggested solutions are correctly classified for the proposed metric when four cases are retrieved. Using a dedicated similarity measure, with relevant and weighted attributes selected through a clinical decision tree, the set of retrieved cases, and consequently, the decision suggested by the case-based reasoning are substantially improved over state-of-the-art similarity measures.
Highlights
Aortic stenosis (AS) is the most commonly occurring valvular heart disease [1], and its severity, and prognosis are diagnosed using echocardiography
In order to support clinical decisions pertaining to vascular access and prosthesis choices in transcatheter aortic valve implantation (TAVI), we focused on the similarity measure which is a key component of the case-based reasoning (CBR)
To examine further the importance of using the clinical decision tree (CDT) for the selection of attributes, we presented the results that were obtained with the similarity measure that do not propose the selection of relevant attributes
Summary
Aortic stenosis (AS) is the most commonly occurring valvular heart disease [1], and its severity, and prognosis are diagnosed using echocardiography. The management of patients is performed by a multi-disciplinary team. This “heart team”, which consists in part of cardiologists, cardiac surgeons, imaging specialists, anesthetists and cardiovascular nursing professionals, have to consider several issues before making decisions [1,2]. Similarity measures and attribute selection for case-based reasoning in TAVI and analysis, decision to publish, or preparation of the manuscript. Therenva provided support in the form of salaries for author FL, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author are articulated in the ‘author contributions’ section
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