Evidence map›Paper›PMID 34057414›Full record

ArticleJMIR medical informatics2021

Head and Tail Entity Fusion Model in Medical Knowledge Graph Construction: Case Study for Pituitary Adenoma.

An Fang, Pei Lou, Jiahui Hu, Wanqing Zhao, Ming Feng, Huiling Ren, Xianlai Chen

Abstract read
In one paragraph

Article in JMIR medical informatics, 2021. 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.

  1. Review
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

7 authors.

An FangLife Science College, Central South University, Changsha, China.ORCID https://orcid.org/0000-0002-9526-9306
Pei LouInstitute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0003-1426-670X
Jiahui HuInstitute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0003-4352-3250
Wanqing ZhaoInstitute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0003-3705-5737
Ming FengPeking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.ORCID https://orcid.org/0000-0001-9943-5941
Huiling RenInstitute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0002-1067-408X
Xianlai ChenBig Data Institute, Central South University, Changsha, China.ORCID https://orcid.org/0000-0002-4338-015X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPituitary adenoma is one of the most common central nervous system tumors. The diagnosis and treatment of pituitary adenoma remain very difficult. Misdiagnosis and recurrence often occur, and experienced neurosurgeons are in serious shortage. A knowledge graph can help interns quickly understand the medical knowledge related to pituitary tumor.

objectiveThe aim of this study was to develop a data fusion method suitable for medical data using data of pituitary adenomas integrated from different sources. The overall goal was to construct a knowledge graph for pituitary adenoma (KGPA) to be used for knowledge discovery.

methodsA complete framework suitable for the construction of a medical knowledge graph was developed, which was used to build the KGPA. The schema of the KGPA was manually constructed. Information of pituitary adenoma was automatically extracted from Chinese electronic medical records (CEMRs) and medical websites through a conditional random field model and newly designed web wrappers. An entity fusion method is proposed based on the head-and-tail entity fusion model to fuse the data from heterogeneous sources.

resultsData were extracted from 300 CEMRs of pituitary adenoma and 4 health portals. Entity fusion was carried out using the proposed data fusion model. The F1 scores of the head and tail entity fusions were 97.32% and 98.57%, respectively. Triples from the constructed KGPA were selected for evaluation, demonstrating 95.4% accuracy.

conclusionsThis paper introduces an approach to fuse triples extracted from heterogeneous data sources, which can be used to build a knowledge graph. The evaluation results showed that the data in the KGPA are of high quality. The constructed KGPA can help physicians in clinical practice.

Indexed as

entity fusionknowledge graphpituitary adenomasimilarity calculation

Identifiers

PMID34057414
PMCPMC8367125

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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.