ReviewHealthcare informatics research2017
Text Mining in Biomedical Domain with Emphasis on Document Clustering.
Review in Healthcare informatics research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Text mining for contexts and relationships in cancer genomics literature.Bioinformatics (Oxford, England) · 2024Article
- The landscape of miRNA-mRNA regulatory network and cellular sources in inflammatory bowel diseases: insights from text mining and single cell RNA sequencing analysis.Frontiers in immunology · 2024Article
- Unsupervised learning and natural language processing highlight research trends in a superbug.Frontiers in artificial intelligence · 2024Article
- RDBridge: a knowledge graph of rare diseases based on large-scale text mining.Bioinformatics (Oxford, England) · 2023Article
- Text Mining of Biomedical Articles Using the Konstanz Information Miner (KNIME) Platform: Hemolytic Uremic Syndrome as a Case Study.Healthcare informatics research · 2022Article
- Making Biomedical Sciences publications more accessible for machines.Medicine, health care, and philosophy · 2022Article
- Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence.Journal of personalized medicine · 2021Article
- Deep learning-based clustering approaches for bioinformatics.Briefings in bioinformatics · 2021Review
- An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering.Computational and mathematical methods in medicine · 2021Article
- Literature Mining and Mechanistic Graphical Modelling to Improve mRNA Vaccine Platforms.Frontiers in immunology · 2021Review
- Structure of communities in semantic networks of biomedical research on disparities in health and sexismBiomedica : revista del Instituto Nacional de Salud · 2020Review
- A Novel Text-Mining Approach for Retrieving Pharmacogenomics Associations From the Literature.Frontiers in pharmacology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWith the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents.
methodsThis paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain.
resultsText mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail.
conclusionsText mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.
Indexed as
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.