Using Analogical Complexes to Improve Human Reasoning and Decision Making in Electronic Health Record Systems

A key ability of human reasoning is analogical reasoning. In this context, an important notion is that of analogical proportions that have been formalized and analyzed in the last decade. A bridging to Formal Concept Analysis (FCA) has been brought by introducing analogical complexes, i.e. formal concepts that share a maximal analogical relation enabling by this analogies between (formal) concepts. Electronic Health Record (EHR) systems are nowadays widespread and used in different scenarios. In this paper we consider the problem of improving EHR systems by using analogical complexes in an FCA based setting. Moreover, we present a study case of analogical complexes in a medical field. We analyze analogical proportions in Electronic Health Record Systems and prove that EHRs can be improved with an FCA grounded analogical reasoning component. This component offers methods for knowledge discovery and knowledge acquisition for medical experts based on patterns revealed by analogies. We also show that combining analogical reasoning with FCA brings a new perspective on the analyzed data that can improve the understanding of the subsequent knowledge structures and offering a valuable support for decision making.

[1]  Diana Halita,et al.  Is FCA suitable to improve Electronic Health Record systems? , 2016, 2016 24th International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

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

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

[4]  Henri Prade,et al.  From analogical proportions in lattices to proportional analogies in formal concepts , 2014, ECAI.

[5]  Henri Prade,et al.  Looking for Analogical Proportions in a Formal Concept Analysis Setting , 2011, CLA.

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

[7]  Soni Jyoti,et al.  Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction , 2011 .

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

[9]  Christian Sacarea,et al.  Symptoms investigation by means of formal concept analysis for enhancing medical diagnoses , 2017, 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

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

[11]  Gilles Richard,et al.  Trying to Understand How Analogical Classifiers Work , 2012, SUM.

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

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

[14]  Rudolf Wille Conceptual Contents as Information - Basics for Contextual Judgment Logic , 2003, ICCS.

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

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

[17]  Gabriela Ferraro,et al.  Benchmarking Clinical Speech Recognition and Information Extraction: New Data, Methods, and Evaluations , 2015, JMIR medical informatics.

[18]  A. Majumdar,et al.  Analogical Reasoning , 2003 .

[19]  Jie Chen,et al.  Mining risk patterns in medical data , 2005, KDD '05.

[20]  Laurent Miclet,et al.  From Formal Concepts to Analogical Complexes , 2015, CLA.