Evidence map›Paper›PMID 37350878›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2023

Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media.

Yuting Guo, Swati Rajwal, Sahithi Lakamana, Chia-Chun Chiang, Paul C Menell, Adnan H Shahid, Yi-Chieh Chen Pharm D, Nikita Chhabra, Wan-Ju Chao, Chieh-Ju Chao and 3 more

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Application of Artificial Intelligence in the Headache Field.Current pain and headache reports · 2024
    Review
  5. Article
  6. 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

13 authors.

Yuting GuoDepartment of Biomedical Informatics, Emory University, Atlanta, GA.
Swati RajwalDepartment of Computer Science, NSUT, India.
Sahithi LakamanaDepartment of Biomedical Informatics, Emory University, Atlanta, GA.
Chia-Chun ChiangDepartment of Neurology, Mayo Clinic, Rochester, MN.
Paul C MenellDepartment of Neurology, Mayo Clinic, Rochester, MN.
Adnan H ShahidDepartment of Neurology, Mayo Clinic, Rochester, MN.
Yi-Chieh Chen Pharm DDepartment of Pharmacy Services, Mayo Clinic Health System, Austin, MN.
Nikita ChhabraDepartment of Neurology, Mayo Clinic, Scottsdale, AZ.
Wan-Ju ChaoDepartment of Psychology, University of North Texas, TX.
Chieh-Ju ChaoDepartment of Cardiology, Mayo Clinic, Rochester, MN.
Todd J SchwedtDepartment of Neurology, Mayo Clinic, Scottsdale, AZ.
Imon BanerjeeDepartment of Radiology, Mayo Clinic, Rochester, AZ.
Abeed SarkerDepartment of Biomedical Informatics, Emory University, Atlanta, GA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Migraine is a highly prevalent and disabling neurological disorder. However, information about migraine management in real-world settings is limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by those with migraine; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts, and used them for training and evaluating supervised machine learning methods. Our best system achieved an F

Identifiers

PMID37350878
PMCPMC10283091

What OpenQuestion holds

Textmetadata
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.