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Journal of biomedical informatics v.79, 2018년, pp.41 - 47   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Exploration of association rule mining for coding consistency and completeness assessment in inpatient administrative health data

Peng, Mingkai (Department of Community Health Sciences, University of Calgary, Calgary, Canada ) ; Sundararajan, Vijaya (Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Australia ) ; Williamson, Tyler (Department of Community Health Sciences, University of Calgary, Calgary, Canada ) ; Minty, Evan P. (Cumming School of Medicine, University of Calgary, Calgary, Canada ) ; Smith, Tony C. (Department of Computer Science, University of Waikato, Hamilton, New Zealand ) ; Doktorchik, Chelsea T.A. (Department of Community Health Sciences, University of Calgary, Calgary, Canada ) ; Quan, Hude (Department of Community Health Sciences, University of Calgary, Calgary, Canada ) ;
  • 초록  

    Abstract Objective Data quality assessment is a challenging facet for research using coded administrative health data. Current assessment approaches are time and resource intensive. We explored whether association rule mining (ARM) can be used to develop rules for assessing data quality. Materials and methods We extracted 2013 and 2014 records from the hospital discharge abstract database (DAD) for patients between the ages of 55 and 65 from five acute care hospitals in Alberta, Canada. The ARM was conducted using the 2013 DAD to extract rules with support ≥0.0019 and confidence ≥0.5 using the bootstrap technique, and tested in the 2014 DAD. The rules were compared against the method of coding frequency and assessed for their ability to detect error introduced by two kinds of data manipulation: random permutation and random deletion. Results The association rules generally had clear clinical meanings. Comparing 2014 data to 2013 data (both original), there were 3 rules with a confidence difference >0.1, while coding frequency difference of codes in the right hand of rules was less than 0.004. After random permutation of 50% of codes in the 2014 data, average rule confidence dropped from 0.72 to 0.27 while coding frequency remained unchanged. Rule confidence decreased with the increase of coding deletion, as expected. Rule confidence was more sensitive to code deletion compared to coding frequency, with slope of change ranging from 1.7 to 184.9 with a median of 9.1. Conclusion The ARM is a promising technique to assess data quality. It offers a systematic way to derive coding association rules hidden in data, and potentially provides a sensitive and efficient method of assessing data quality compared to standard methods. Highlights Exploration of association rule mining for data quality rule development. The derived coding association rules had clearly clinical meanings. Rules can efficiently check coding consistency and completeness at high granularity. Graphical abstract [DISPLAY OMISSION]


  • 주제어

    Coding completeness .   Coding inconsistency .   Association rule mining .   Inpatient administrative health data .   Diagnosis code .   International classification of disease.  

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