Literature DB >> 7949912

An evaluation of computer assisted clinical classification algorithms.

C G Chute1, Y Yang, J Buntrock.   

Abstract

The Mayo Clinic has a long tradition of indexing patient records in high resolution and volume. Several algorithms have been developed which promise to help human coders in the classification process. We evaluate variations on code browsers and free text indexing systems with respect to their speed and error rates in our production environment. The more sophisticated indexing systems save measurable time in the coding process, but suffer from incompleteness which requires a back-up system or human verification. Expert Network does the best job of rank ordering clinical text, potentially enabling the creation of thresholds for the pass through of computer coded data without human review.

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Year:  1994        PMID: 7949912      PMCID: PMC2247905     

Source DB:  PubMed          Journal:  Proc Annu Symp Comput Appl Med Care        ISSN: 0195-4210


  1 in total

1.  The patient record in epidemiology.

Authors:  L T Kurland; C A Molgaard
Journal:  Sci Am       Date:  1981-10       Impact factor: 2.142

  1 in total
  4 in total

1.  A hybrid approach to determining modification of clinical diagnoses.

Authors:  Sergeui Pakhomov; Christopher G Chute
Journal:  AMIA Annu Symp Proc       Date:  2006

2.  Automating the assignment of diagnosis codes to patient encounters using example-based and machine learning techniques.

Authors:  Serguei V S Pakhomov; James D Buntrock; Christopher G Chute
Journal:  J Am Med Inform Assoc       Date:  2006-06-23       Impact factor: 4.497

3.  An evaluation of statistical approaches to MEDLINE indexing.

Authors:  Y Yang
Journal:  Proc AMIA Annu Fall Symp       Date:  1996

4.  Sampling strategies in a statistical approach to clinical classification.

Authors:  Y Yang; C G Chute
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1995
  4 in total

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