Difference between revisions of "Natural language processing and inference rules as strategies for updating problem list in an electronic health record"
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===Conclusion=== | ===Conclusion=== | ||
| + | Natural language processing and inference rules have had acceptable results as tools for incorporating health problems into a problem list, mainly using limited sets of data. Further studies are needed to validate these rules in other areas and to extend the tools to a more comprehensively. | ||
==Comments== | ==Comments== | ||
Revision as of 23:32, 11 November 2015
IN PROGRESS. [1]
Contents
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
Natural language processing and inference rules have had acceptable results as tools for incorporating health problems into a problem list, mainly using limited sets of data. Further studies are needed to validate these rules in other areas and to extend the tools to a more comprehensively.
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
- ↑ 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.
- ↑ 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.
