Knowledge Extraction from MEDLINE by Combining Clustering with Natural Language Processing.

Jose A Miñarro-Giménez, Markus Kreuzthaler, Stefan Schulz

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Creation of Individual Scientific Concept-Centered Semantic Maps Based on Automated Text-Mining Analysis of PubMed.Advances in bioinformatics · 2018
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

3 authors.

Jose A Miñarro-GiménezInstitute of Medical Informatics, Statistics, and Documentation, Medical University of Graz, Austria.

Markus KreuzthalerInstitute of Medical Informatics, Statistics, and Documentation, Medical University of Graz, Austria.

Stefan SchulzInstitute of Medical Informatics, Statistics, and Documentation, Medical University of Graz, Austria.

Funding

Austrian Science Fund FWF M 1729

6 · The paper itself

Abstract

The identification of relevant predicates between co-occurring concepts in scientific literature databases like MEDLINE is crucial for using these sources for knowledge extraction, in order to obtain meaningful biomedical predications as subject-predicate-object triples. We consider the manually assigned MeSH indexing terms (main headings and subheadings) in MEDLINE records as a rich resource for extracting a broad range of domain knowledge. In this paper, we explore the combination of a clustering method for co-occurring concepts based on their related MeSH subheadings in MEDLINE with the use of SemRep, a natural language processing engine, which extracts predications from free text documents. As a result, we generated sets of clusters of co-occurring concepts and identified the most significant predicates for each cluster. The association of such predicates with the co-occurrences of the resulting clusters produces the list of predications, which were checked for relevance.

Indexed as

Cluster AnalysisMedical Subject HeadingsMEDLINENatural Language ProcessingDatabases, FactualHumansInformation Storage and RetrievalSemantics

Identifiers

PMID26958228

PMCPMC4765595

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.