Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.
- DOI
- 10.1093/jamia/ocaa218
- Published
- 2021 Mar 1
- Container
- Journal of the American Medical Informatics Association : JAMIA
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1093/jamia/ocaa218,
title = {Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.},
author = {Fan Y and Zhou S and Li Y and Zhang R},
year = {2021},
journal = {Journal of the American Medical Informatics Association : JAMIA},
doi = {10.1093/jamia/ocaa218},
url = {https://doi.org/10.1093/jamia/ocaa218}
}RIS
TY - JOUR TI - Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text. AU - Fan Y AU - Zhou S AU - Li Y AU - Zhang R PY - 2021 JO - Journal of the American Medical Informatics Association : JAMIA DO - 10.1093/jamia/ocaa218 UR - https://doi.org/10.1093/jamia/ocaa218 ER -
APA
Y, F., S, Z., Y, L., & R, Z. (2021). Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.. Journal of the American Medical Informatics Association : JAMIA. https://doi.org/10.1093/jamia/ocaa218
Source records
- pubmed · retrieved 2026-09-25T16:38:08.947Z