An Information Extraction Approach to Detecting Novelty of Biomedical Publications.

Xueqing Peng, Brian Ondov, Huan He, Yan Hu, Hua Xu

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. 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

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.

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

5 authors.

Xueqing PengDepartment of Biomedical Informatics & Data Science, School of Medicine, Yale University, New Haven, CT.

Brian OndovDepartment of Biomedical Informatics & Data Science, School of Medicine, Yale University, New Haven, CT.

Huan HeDepartment of Biomedical Informatics & Data Science, School of Medicine, Yale University, New Haven, CT.

Yan HuMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.

Hua XuDepartment of Biomedical Informatics & Data Science, School of Medicine, Yale University, New Haven, CT.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Scientific novelty plays a critical role in shaping research impact, yet it remains inconsistently defined and difficult to quantify. Existing approaches often reduce novelty to a single measure, failing to distinguish the specific types of contributions (such as new concepts or relationships) that drive influence. In this study, we introduce a semantic measure of novelty based on the emergence of new biomedical entities and relationships within the conclusion sections of research articles. Leveraging transformer-based named entity recognition (NER) and relation extraction (RE) tools, we identify novel findings and classify articles into four categories: No Novelty, Entity-only Novelty, Relation-only Novelty, and Entity-Relation Novelty. We evaluate this framework using citation counts and Journal Impact Factors (JIF) as proxies for research influence. Our results show that Entity-Relation Novelty articles receive the highest citation impact, with relation novelty more closely aligned with high-impact journals. These findings offer a scalable framework for assessing novelty and guiding future research evaluation.

Indexed as

Biomedical ResearchData MiningInformation Storage and RetrievalNatural Language ProcessingPeriodicals as TopicJournal Impact FactorSemantics

Identifiers

PMID41726468

PMCPMC12919535

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.