Natural language processing and inference rules as strategies for updating problem list in an electronic health record

From Clinfowiki
Revision as of 23:31, 11 November 2015 by TheoBiblio (Talk | contribs)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

IN PROGRESS. [1]

Abstract[2]

Background

Physicians do not always keep the problem list accurate, complete and updated.

Objective

To analyze natural language processing (NLP) techniques and inference rules as strategies to maintain completeness and accuracy of the problem list in EHRs.

Materials and Methods

Non systematic literature review in PubMed, in the last 10 years. Strategies to maintain the EHRs problem list were analyzed in two ways: inputting and removing problems from the problem list.

Results

NLP and inference rules have acceptable performance for inputting problems into the problem list. No studies using these techniques for removing problems were published Conclusion: Both tools, NLP and inference rules have had acceptable results as tools for maintain the completeness and accuracy of the problem list.

Discussion

Conclusion

Comments

IN PROGRESS.

Related Resources

Using natural language processing to identify problem usage of prescription opioids

Visualizing unstructured patient data for assessing diagnostic and therapeutic history

References

  1. Plazzotta F, Otero C, Luna D, De quiros FG. Natural language processing and inference rules as strategies for updating problem list in an electronic health record. Stud Health Technol Inform. 2013;192:1163.
  2. Wright A, McCoy AB, Henkin S, Kale A, Sittig DF. Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions. Journal of the American Medical Informatics Association : JAMIA. 2013;20(5):887-890. doi:10.1136/amiajnl-2012-001576.