Abstract
The combination of visual and textual information in a CBR system is a promising concept to overcome the limitations of existing medical CBR systems, which are mainly focused on evaluation of new and existing cases with data mining, clustering techniques or statistical analysis of patient’s health condition parameters. The advantage of our proposed medical CBR system, called DePicT, is the knowledge based recommendation, which utilizes case-based reasoning through analyzing image and text from patient health records. DePicT can find a solution regarding patient’s problem description even with partly missing information. It uses image interpretation parameters and profiles of word associations in the feature selection and case matching process to find similar cases for recommendation.
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Nasiri, S., Zenkert, J., Fathi, M. (2015). A Medical Case-Based Reasoning Approach Using Image Classification and Text Information for Recommendation. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_4
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DOI: https://doi.org/10.1007/978-3-319-19222-2_4
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