{"paper_id":"b2753d87-0b6f-49e6-83d5-e87d5f9dda96","body_text":"Abstract\nBecause clinical research is carried out in complex environments, prior domain knowledge, constraints, and expert knowledge can enhance the capabilities and performance of data mining. In this paper we propose an unexpected pattern mining model that uses decision trees to compare recovery rates of two different treatments, and to find patterns that contrast with the prior knowledge of domain users. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. By applying the concept of domain-driven data mining, we repeatedly utilize decision trees and interestingness measures in a closed-loop, in-depth mining process to find unexpected and interesting patterns. We use retrospective data from transvaginal ultrasound-guided aspirations to show that the proposed model can successfully compare different treatments using a decision tree, which is a new usage of that tool. We believe that unexpected, interesting patterns may provide clinical researchers with different perspectives for future research.\nSimilar content being viewed by others\nAbbreviations\n- ANOVA:\n-\nAnalysis of variance\n- D\\(^{3}\\)M:\n-\nDomain-driven data mining\n- CA-125:\n-\nCancer antigen 125, carcinoma antigen 125, or carbohydrate antigen 125\n- BMI:\n-\nBody mass index\n- CART:\n-\nClassification and regression tree\n- EST:\n-\nEthanol sclerotherapy\n- ID3:\n-\nIterative Dichotomiser 3 algorithm\n- CHAID:\n-\nChi-Square Automatic Interaction Detector\n- tech():\n-\nTechnical interestingness measures\n- biz():\n-\nBusiness interestingness measures\n- act():\n-\nActionability of a pattern\n- tech_obj():\n-\nTechnical objective interestingness measures\n- tech_sub():\n-\nTechnical subjective interestingness measures\n- biz_obj():\n-\nBusiness objective interestingness measures\n- biz_sub():\n-\nBusiness subjective interestingness measures\n- RecoveryRate():\n-\nProbability patient recovers from illness\n- IM \\(_{\\mathrm{tech\\_obj}}\\) :\n-\nTechnical objective interestingness measure\n- IM \\(_{\\mathrm{tech\\_sub}}\\) :\n-\nTechnical subjective interestingness measure\n- IM \\(_{\\mathrm{biz\\_obj}}\\) :\n-\nBusiness objective interestingness measure\n- IM \\(_{\\mathrm{biz\\_sub}}\\) :\n-\nBusiness subjective interestingness measure\nReferences\nBaena-García M, Morales-Bueno R (2012) Mining interestingness measures for string pattern mining. 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Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis. Soft Comput 20, 3991–4003 (2016). https://doi.org/10.1007/s00500-015-1735-0\nPublished:\nIssue date:\nDOI: https://doi.org/10.1007/s00500-015-1735-0","source_license":"CC0","license_restricted":false}