Evidence map›Paper›PMID 36050787›Full record

ArticleDatabase : the journal of biological databases and curation2022

Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers.

Graciela Gonzalez-Hernandez, Martin Krallinger, Monica Muñoz, Raul Rodriguez-Esteban, Özlem Uzuner, Lynette Hirschman

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Biomedical relation extraction with knowledge base-refined weak supervision.Database : the journal of biological databases and curation · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Graciela Gonzalez-HernandezDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, 700 N. San Vicente Blvd., West Hollywood, CA 90069, USA.
Martin KrallingerLife Sciences-Text Mining, Barcelona Supercomputing Center, Plaça Eusebi Güell, 1-3, Barcelona 08034, Spain.
Monica MuñozDivision of Pharmacovigilance, Office of Surveillance and Epidemiology, Center of Drug Evaluation and Research, FDA, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA.
Raul Rodriguez-EstebanRoche Innovation Center Basel, Roche Pharmaceuticals, Grenzacherstrasse 124, Basel 4070, Switzerland.
Özlem UzunerInformation Sciences and Technology, George Mason University, 4400 University Dr, Fairfax, VA 22030, USA.
Lynette HirschmanMITRE Labs, The MITRE Corporation, 202 Burlington Rd., Bedford, MA 01730, USA.ORCID 0000-0001-8442-0720

Funding

Social Media Mining for PharmacovigilanceR01LM011176 · NLM · UNIVERSITY OF PENNSYLVANIA · PI GONZALEZ HERNANDEZ, GRACIELA, SHEN, LI · 2012 to 2021
$4.5M
Leveraging Unlabeled and Pseudo Data for Clinical Information ExtractionR15LM013209 · NLM · GEORGE MASON UNIVERSITY · PI UZUNER, OZLEM · 2019 to 2022
$840k
National NLP Clinical Challenges (n2c2): Challenges in Natural Language Processing for Clinical NarrativesR13LM013127 · NLM · GEORGE MASON UNIVERSITY · PI UZUNER, OZLEM · 2019 to 2023
$100k
NLM NIH HHS R01 LM011176NLM NIH HHS R13 LM013127NLM NIH HHS R15 LM013209
6 · The paper itself

Abstract

Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore 'Challenges in Mining Drug Adverse Reactions'. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.

Indexed as

Artificial IntelligenceComputational BiologyData MiningHealth PersonnelHumansNatural Language Processing

Identifiers

PMID36050787
PMCPMC9436770

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.