Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses |
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| Authors: | Y. Sebastian Patrick H.H. Then |
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| Affiliation: | 1. Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai;2. Department of Oncology, Fudan University Shanghai Medical College, Shanghai;3. Department of Pathology, Cancer Institute and Hospital, Chinese Academy of Medical Science, Beijing;4. Department of Pathology, Guangdong General Hospital, Guangzhou;5. Department of Pathology, West China Hospital of Sichuan University, Chengdu, China;1. Grup de Recerca en Sistemes Intelligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, 08022 Barcelona, Spain;2. Dep. of Business Administration, Business Faculty, University of Granada, Campus Cartuja s/n, 18.071 Granada, Spain;3. Marketing Group, Dep. of Economic and Business Studies, Open University of Catalonia, Barcelona, Spain;4. Dep. of Computer Science and Artificial Intelligence, Computer and Telecommunication Engineering School, 18.071 Granada, Spain;5. Marketing Dep., Aston Business School, Aston Triangle, Birmingham, UK, B4 7ET |
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| Abstract: | IntroductionAn important quality of association rules is novelty. However, evaluating rule novelty is AI-hard and has been a serious challenge for most data mining systems.ObjectiveIn this paper, we introduce functional novelty, a new non-pairwise approach to evaluating rule novelty. A functionally novel rule is interesting as it suggests previously unknown relations between user hypotheses.MethodsWe developed a novel domain-driven KDD framework for discovering functionally novel association rules. Association rules were mined from cardiovascular data sets. At post-processing, domain knowledge-compliant rules were discovered by applying semantic-based filtering based on UMLS ontology. Their knowledge compliance scores were computed against medical knowledge in Pubmed literature. A cardiologist explored possible relationships between several pairs of unknown hypotheses. The functional novelty of each rule was computed based on its likelihood to mediate these relationships.ResultsHighly interesting rules were successfully discovered. For instance, common rules such as diabetes mellitus?coronary arteriosclerosis was functionally novel as it mediated a rare association between von Willebrand factor and intracardiac thrombus.ConclusionThe proposed post-mining domain-driven rule evaluation technique and measures proved to be useful for estimating candidate functionally novel rules with the results validated by a cardiologist. |
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