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. 2021 Aug:120:103851.
doi: 10.1016/j.jbi.2021.103851. Epub 2021 Jun 24.

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health

Ruth M Reeves  1 , Lee Christensen  2 , Jeremiah R Brown  3 , Michael Conway  2 , Maxwell Levis  3 , Glenn T Gobbel  4 , Rashmee U Shah  5 , Christine Goodrich  3 , Iben Ricket  3 , Freneka Minter  6 , Andrew Bohm  3 , Bruce E Bray  7 , Michael E Matheny  8 , Wendy Chapman  9
Affiliations

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health

Ruth M Reeves et al. J Biomed Inform. 2021 Aug.

Abstract

Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort. We developed a transformation of the SDoH outputs of the tool into the OMOP common data model (CDM) for re-use across many potential use cases, yielding performance measures across 8 SDoH classes of precision 0.83 recall 0.74 and F-measure of 0.78.

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Figures

Fig 1.
Fig 1.
Typed Semantic Rule example.
Fig 2.
Fig 2.
Moonstone Ontology Tool.
Fig 3.
Fig 3.
Firing a Grammar Rule.
Figure 4:
Figure 4:
Automatically generated “snapshot rule”
Figure 5:
Figure 5:
Headers in Relation to Grammar Rules
See this image and copyright information in PMC

References

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