Is FCA suitable to improve Electronic Health Record systems?

Formal Concept Analysis (FCA) is a prominent field of applied mathematics using formal concepts - maximal clusters of object-attribute relationships - to discover, process, and represent knowledge in so-called conceptual hierarchies. Its efficient algorithms and expressive power makes FCA suitable to unify methodologies and to provide an in-depth insight on knowledge structures [1], [2]. Electronic Health Record (EHR) systems are nowadays widespread and used in different scenarios. In this paper we consider the problem of improving EHR systems with new, FCA grounded features. For this, we start with some particular medical data sets and discuss the improvement of some features of EHR systems by using FCA. Two main methods have been taken into consideration so far. First, we consider the medical data sets as many-valued contexts. By using conceptual scaling, we build `knowledge landscapes' [3] and show how these `landscapes' might be used in the framework of EHR. A complementary approach is based on Triadic FCA (3FCA) approach. We exemplify these methods on several medical datasets and discuss how conceptual landscapes can be used to improve not only the integrated view of patient data (as an EHR system specific feature), but also communication and support future research.

[1]  Rudolf Wille,et al.  Why can concept lattices support knowledge discovery in databases? , 2002, J. Exp. Theor. Artif. Intell..

[2]  Peter Becker,et al.  The ToscanaJ Suite for Implementing Conceptual Information Systems , 2005, Formal Concept Analysis.

[3]  Amir Hassan Zadeh,et al.  Predicting overall survivability in comorbidity of cancers: A data mining approach , 2015, Decis. Support Syst..

[4]  Gari D. Clifford,et al.  Shortliffe Edward H, Cimino James J: "Biomedical Informatics; Computer Applications in Health Care and Biomedicine" , 2006 .

[5]  Christian Sacarea.  Investigating Oncological Databases Using Conceptual Landscapes , 2014, ICCS.

[6]  Makoto Haraguchi,et al.  Data Mining of Time-Series Medical Data by Formal Concept Analysis , 2007, KES.

[7]  Rudolf Wille,et al.  Methods of Conceptual Knowledge Processing , 2006, ICFCA.

[8]  Rudolf Wille,et al.  A Triadic Approach to Formal Concept Analysis , 1995, ICCS.

[9]  Rudolf Wille,et al.  Conceptual Landscapes of Knowledge: A Pragmatic Paradigm for Knowledge Processing , 1999 .

[10]  Christian Sacarea,et al.  Knowledge visualization for supporting communication in cardiovascular risk assessment hypotheses , 2015, 2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

[11]  Christian Sacarea,et al.  Investigating adverse drug reactions using Conceptual Landscapes , 2011, 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing.

[12]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[13]  DelenDursun,et al.  Predicting overall survivability in comorbidity of cancers , 2015 .

[14]  N. Menachemi,et al.  Benefits and drawbacks of electronic health record systems , 2011 .

[15]  E. Shortliffe,et al.  Biomedical informatics : computer applications in health care and biomedicine , 2001 .

[16]  Christian Sacarea,et al.  A contextual logic grounded approach to visual arts , 2011, 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing.

[17]  Amedeo Napoli,et al.  Using Formal Concept Analysis for Mining and Interpreting Patient Flows within a Healthcare Network , 2006, CLA.

[18]  Rudolf Wille.  Conceptual Knowledge Processing: Theory and Practice , 2007, KONT/KPP.

[19]  Dursun Delen,et al.  Predicting breast cancer survivability: a comparison of three data mining methods , 2005, Artif. Intell. Medicine.

[20]  Robin C. Meili,et al.  Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. , 2005, Health affairs.

[21]  Christoph U. Lehmann,et al.  Meaningful Use of Electronic Health Records: Experiences From the Field and Future Opportunities , 2015, JMIR medical informatics.