ArticlePloS one
SparkText: Biomedical Text Mining on Big Data Framework.
Article in PloS one. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed.
- Merging data curation and machine learning to improve nanomedicines.Advanced drug delivery reviews · 2022Review
- Deep learning in multimedia healthcare applications: a review.Multimedia systems · 2022Article
- DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases.Oxidative medicine and cellular longevity · 2020Article
- Sustainable Health Informatics: Health Informaticians as Alchemists.Studies in health technology and informatics · 2019Article
- Literature-Based Enrichment Insights into Redox Control of Vascular Biology.Oxidative medicine and cellular longevity · 2019Review
- Global Genetics Research in Prostate Cancer: A Text Mining and Computational Network Theory Approach.Frontiers in genetics · 2019Article
- Discovering major opioid-related research themes over time: A text mining technique.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Collaborative and Reproducible Research: Goals, Challenges, and Strategies.Journal of digital imaging · 2018Article
- Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support.Applied clinical informatics · 2018Article
- Big Data Analytics for Genomic Medicine.International journal of molecular sciences · 2017Review
- Revealing topics and their evolution in biomedical literature using Bio-DTM: a case study of ginseng.Chinese medicine · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundMany new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment.
resultsIn this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes.
conclusionsThis study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.
Identifiers
What OpenQuestion holds
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.