Evidence map›Paper›PMID 37128369›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2022

Towards User-centered Corpus Development: Lessons Learnt from Designing and Developing MedTator.

Huan He, Sunyang Fu, Liwei Wang, Andrew Wen, Sijia Liu, Sungrim Moon, Kurt Miller, Hongfang Liu

Open access · greenAbstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 85% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 1 institution in 1 country.

Huan HeDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
Sunyang FuDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
Liwei WangDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
Andrew WenDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
Sijia LiuDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
Sungrim MoonDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
Kurt MillerDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
Hongfang LiuDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
Mayo Clinic in Florida · US

Funding

Open Health Natural Language Processing CollaboratoryU01TR002062 · NCATS · MAYO CLINIC ROCHESTER · PI JIANG, XIAOQIAN, LIU, HONGFANG · 2017 to 2021
$7.6M
NCATS NIH HHS U01 TR002062
6 · The paper itself

Abstract

A gold standard annotated corpus is usually indispensable when developing natural language processing (NLP) systems. Building a high-quality annotated corpus for clinical NLP requires considerable time and domain expertise during the annotation process. Existing annotation tools may provide powerful features to cover various needs of text annotation tasks, but the target end users tend to be trained annotators. It is challenging for clinical research teams to utilize those tools in their projects due to various factors such as the complexity of advanced features and data security concerns. To address those challenges, we developed MedTator, a serverless web-based annotation tool with an intuitive user-centered interface aiming to provide a lightweight solution for the core tasks in corpus development. Moreover, we present three lessons learned from the designing and developing MedTator, which will contribute to the research community's knowledge for future open-source tool development.

Indexed as

Natural Language ProcessingHumans

Identifiers

PMID37128369
PMCPMC10148300
OpenAlexW4367668818

What Socratic 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 Socratic graph.