ArticleBMC medical informatics and decision making2017
Semantic relatedness and similarity of biomedical terms: examining the effects of recency, size, and section of biomedical publications on the performance of word2vec.
Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Benchmarking MeSH-augmented embeddings for biomedical document similarity.Journal of biomedical semantics · 2026Article
- Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.Psychonomic bulletin & review · 2023Article
- Validating the representation of distance between infarct diseases using word embedding.BMC medical informatics and decision making · 2022Article
- The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model.Current psychology (New Brunswick, N.J.) · 2022Article
- An Ensemble Semantic Textual Similarity Measure Based on Multiple Evidences for Biomedical Documents.Computational and mathematical methods in medicine · 2022Article
- Model-Based Reasoning of Clinical Diagnosis in Integrative Medicine: Real-World Methodological Study of Electronic Medical Records and Natural Language Processing Methods.JMIR medical informatics · 2020Article
- Better synonyms for enriching biomedical search.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Characterization of near death experiences using text mining analyses: A preliminary study.PloS one · 2020Article
- Feature-Based Learning in Drug Prescription System for Medical Clinics.Neural processing letters · 2020Article
- Automated grouping of medical codes via multiview banded spectral clustering.Journal of biomedical informatics · 2019Article
- Neural networks for mining the associations between diseases and symptoms in clinical notes.Health information science and systems · 2019Article
- Unsupervised low-dimensional vector representations for words, phrases and text that are transparent, scalable, and produce similarity metrics that are not redundant with neural embeddings.Journal of biomedical informatics · 2019Article
- A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering.Entropy (Basel, Switzerland) · 2018Article
- Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases.BMC medical informatics and decision making · 2018Article
- A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.PLoS computational biology · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnderstanding semantic relatedness and similarity between biomedical terms has a great impact on a variety of applications such as biomedical information retrieval, information extraction, and recommender systems. The objective of this study is to examine word2vec's ability in deriving semantic relatedness and similarity between biomedical terms from large publication data. Specifically, we focus on the effects of recency, size, and section of biomedical publication data on the performance of word2vec.
methodsWe download abstracts of 18,777,129 articles from PubMed and 766,326 full-text articles from PubMed Central (PMC). The datasets are preprocessed and grouped into subsets by recency, size, and section. Word2vec models are trained on these subtests. Cosine similarities between biomedical terms obtained from the word2vec models are compared against reference standards. Performance of models trained on different subsets are compared to examine recency, size, and section effects.
resultsModels trained on recent datasets did not boost the performance. Models trained on larger datasets identified more pairs of biomedical terms than models trained on smaller datasets in relatedness task (from 368 at the 10% level to 494 at the 100% level) and similarity task (from 374 at the 10% level to 491 at the 100% level). The model trained on abstracts produced results that have higher correlations with the reference standards than the one trained on article bodies (i.e., 0.65 vs. 0.62 in the similarity task and 0.66 vs. 0.59 in the relatedness task). However, the latter identified more pairs of biomedical terms than the former (i.e., 344 vs. 498 in the similarity task and 339 vs. 503 in the relatedness task).
conclusionsIncreasing the size of dataset does not always enhance the performance. Increasing the size of datasets can result in the identification of more relations of biomedical terms even though it does not guarantee better precision. As summaries of research articles, compared with article bodies, abstracts excel in accuracy but lose in coverage of identifiable relations.
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Registered trials
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.