Paper: Christian Săcărea, Diana Șotropa, Diana Troancă, Symptoms investigation by means of Formal Concept Analysis for enhancing medical diagnoses, Proceedings of the 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2017), 21-23 September 2017, pp. 1-5, Split, Croatia
Abstract: Pattern extraction is one of the major topics in Knowledge Discovery. Out of numerous data mining techniques we propose to use a new approach: Formal Concept Analysis (FCA) together with Graph Databases with the implementation Neo4j. FCA is a prominent field of applied mathematics using maximal clusters of object-attribute relationships to discover and represent knowledge structures. The use of FCA gained much importance in many research domains in recent years. Despite the similarity of the graph representation between FCA and Neo4j, the structure and relations among the elements are represented differently and can offer a different perspective on the analysis.
This paper gives more insight into how patterns can be extracted from medical data and interpreted by means of FCA and Neo4j in order to help medical staff improve the accuracy of diagnoses. We examine the factors related to patients’ symptoms and diagnostics and then compare the results with the ones provided by a medical care center.
We apply knowledge discovery techniques and conceptual landscapes paradigm in order to obtain an in-depth and high qualitative knowledge representation of medical data.
By making use of the effectiveness and the graphical representation of conceptual hierarchies we extract valuable knowledge from medical data sets through which we solve the knowledge discovery, processing and representation task in Electronic Health Record systems.
Keywords: Formal Concept Analysis, Electronic Health Record, medical data, many-valued contexts, conceptual scaling, Triadic FCA, Neo4J
Acknowledgments: Diana Şotropa was supported by Tora Trading Services private scholarship.
Presentation: S1: SOFTWARE BASED SYSTEMS AND SERVICES