Extraction of medical pathways from electronic patient records

With the introduction of electronic medical records, a large amount of patients' medical data has been available. An actual problem in this domain is to perform reverse engineering of the medical treatment process to highlight medical pathways typically adopted for specific health conditions. This chapter addresses the ability of sequential data mining techniques to reconstruct the actual medical pathways followed by patients. Detected medical pathways are in the form of sets of exams frequently done together, sequences of exam sets frequently followed by patients and frequent correlations between exam sets. The analysis shows that the majority of the extracted pathways are consistent with the medical guidelines, but also reveals some unexpected results, which can be useful both to enrich existing guidelines and to improve the public sanitary service

[1]  Patricia B. Cerrito Mining the Electronic Medical Record to Examine Physician Decisions , 2007, Advanced Computational Intelligence Paradigms in Healthcare.

[2]  K. Sartipi,et al.  Incoporating Data Mining Applications into Clinical Guildelines , 2006, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06).

[3]  Mohammad Reza Keyvanpour,et al.  A Perturbation Method Based on Singular Value Decomposition and Feature Selection for Privacy Preserving Data Mining , 2014, Int. J. Data Warehous. Min..

[4]  Sellappan Palaniappan,et al.  Clinical Decision Support Using OLAP With Data Mining , 2008 .

[5]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[6]  Elena Baralis,et al.  Analysis of Medical Pathways by Means of Frequent Closed Sequences , 2010, KES.

[7]  Wesley W. Chu,et al.  Drug exposure side effects from mining pregnancy data , 2007, SKDD.

[8]  Babak Akhgar,et al.  Automated Diagnosis System to Support Colon Cancer Treatment: MATCH , 2008, Fifth International Conference on Information Technology: New Generations (itng 2008).

[9]  M. Gholamian International Journal of Data Warehousing and Mining , 2014 .

[10]  J. Michael Hardin,et al.  Data Mining and Clinical Decision Support Systems , 2007 .

[11]  A Min Tjoa,et al.  The Relevance of Data Warehousing and Data Mining in the Field of Evidence-based Medicine to Support Healthcare Decision Making , 2007 .

[12]  Anjana Gosain,et al.  Analysis of health care data using different data mining techniques , 2009, 2009 International Conference on Intelligent Agent & Multi-Agent Systems.

[13]  J. Balldin,et al.  High AST/ALT ratio may indicate advanced alcoholic liver disease rather than heavy drinking. , 2004, Alcohol and alcoholism.

[14]  Patrice Degoulet,et al.  Analysis of hospitalised patient flows using data-mining , 2003, MIE.

[15]  Marc Cuggia,et al.  Managing an emergency department by analysing HIS medical data:a focus on elderly patient clinical pathways , 2008, Health care management science.

[16]  Tatsuo Tsuji,et al.  A Parallel Implementation Scheme of Relational Tables Based on Multidimensional Extendible Array , 2006, Int. J. Data Warehous. Min..