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Structuring Free-text Microbiology Culture Reports For Secondary Use.

Yim WW, Evans HL, Yetisgen M - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: However, their incorporation into clinical surveillance applications and evidence-based medicine can be severely hindered by the free-text nature of these reports.System performances were favorable, with entity f1-score 0.889 and relation f1-score 0.795.We plan to incorporate these extractions as features for our ongoing ventilator-associated pneumonia surveillance project, though this tool can be used as an upstream process in other applications.

View Article: PubMed Central - PubMed

Affiliation: Biomedical and Health Informatics, School of Medicine, University of Washington, Seattle, WA.

ABSTRACT
Microbiology lab culture reports are a frequently used diagnostic tool for clinical providers. However, their incorporation into clinical surveillance applications and evidence-based medicine can be severely hindered by the free-text nature of these reports. In this work, we (1) created a microbiology culture template to structure free-text microbiology reports, (2) generated an annotated microbiology report corpus, and (3) built a microbiology information extraction system. Specifically, we combined rule-based, hybrid, and statistical techniques to extract microbiology entities and fill templates for structuring data. System performances were favorable, with entity f1-score 0.889 and relation f1-score 0.795. We plan to incorporate these extractions as features for our ongoing ventilator-associated pneumonia surveillance project, though this tool can be used as an upstream process in other applications. Our newly created corpus includes 1442 unique gram stain and culture microbiology reports generated from a cohort of 715 patients at the University of Washington Medical Facilities.

No MeSH data available.


Related in: MedlinePlus

Overall system architecture
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Related In: Results  -  Collection


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f2-2088316: Overall system architecture

Mentions: We used the annotated corpus to train and evaluate an extraction system. Figure 2 presents the overall system architecture. Entity types were extracted independently using various strategies. Afterwards, all entities were consolidated and entity span overlap conflicts were resolved. Relations were identified from pair-wise classification of system entities.


Structuring Free-text Microbiology Culture Reports For Secondary Use.

Yim WW, Evans HL, Yetisgen M - AMIA Jt Summits Transl Sci Proc (2015)

Overall system architecture
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525274&req=5

f2-2088316: Overall system architecture
Mentions: We used the annotated corpus to train and evaluate an extraction system. Figure 2 presents the overall system architecture. Entity types were extracted independently using various strategies. Afterwards, all entities were consolidated and entity span overlap conflicts were resolved. Relations were identified from pair-wise classification of system entities.

Bottom Line: However, their incorporation into clinical surveillance applications and evidence-based medicine can be severely hindered by the free-text nature of these reports.System performances were favorable, with entity f1-score 0.889 and relation f1-score 0.795.We plan to incorporate these extractions as features for our ongoing ventilator-associated pneumonia surveillance project, though this tool can be used as an upstream process in other applications.

View Article: PubMed Central - PubMed

Affiliation: Biomedical and Health Informatics, School of Medicine, University of Washington, Seattle, WA.

ABSTRACT
Microbiology lab culture reports are a frequently used diagnostic tool for clinical providers. However, their incorporation into clinical surveillance applications and evidence-based medicine can be severely hindered by the free-text nature of these reports. In this work, we (1) created a microbiology culture template to structure free-text microbiology reports, (2) generated an annotated microbiology report corpus, and (3) built a microbiology information extraction system. Specifically, we combined rule-based, hybrid, and statistical techniques to extract microbiology entities and fill templates for structuring data. System performances were favorable, with entity f1-score 0.889 and relation f1-score 0.795. We plan to incorporate these extractions as features for our ongoing ventilator-associated pneumonia surveillance project, though this tool can be used as an upstream process in other applications. Our newly created corpus includes 1442 unique gram stain and culture microbiology reports generated from a cohort of 715 patients at the University of Washington Medical Facilities.

No MeSH data available.


Related in: MedlinePlus