ArticleACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine2015
Convolutional Neural Networks for Biomedical Text Classification: Application in Indexing Biomedical Articles.
Article in ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Article
- Using artificial intelligence to develop a measure of orthopaedic treatment success from clinical notes.Frontiers in digital health · 2025Article
- Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing.Diagnostics (Basel, Switzerland) · 2024Article
- Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.Communications medicine · 2024Article
- Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models.Tribology international · 2023Article
- A Disease-Prediction Protocol Integrating Triage Priority and BERT-Based Transfer Learning for Intelligent Triage.Bioengineering (Basel, Switzerland) · 2023Article
- Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing.JAMA network open · 2023Article
- Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts.Computational intelligence and neuroscience · 2023Article
- Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types.Journal of biomedical informatics · 2022Article
- Weakly Supervised Ternary Stream Data Augmentation Fine-Grained Classification Network for Identifying Acute Lymphoblastic Leukemia.Diagnostics (Basel, Switzerland) · 2021Article
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- Learning adaptive representations for entity recognition in the biomedical domain.Journal of biomedical semantics · 2021Article
- The risk of racial bias while tracking influenza-related content on social media using machine learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- A stacked convolutional neural network for detecting the resource tweets during a disaster.Multimedia tools and applications · 2021Article
- Biomedical document triage using a hierarchical attention-based capsule network.BMC bioinformatics · 2020Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Hybrid deep learning for detecting lung diseases from X-ray images.Informatics in medicine unlocked · 2020Article
- ML-Net: multi-label classification of biomedical texts with deep neural networks.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Cross-registry neural domain adaptation to extract mutational test results from pathology reports.Journal of biomedical informatics · 2019Article
- Distant supervision for treatment relation extraction by leveraging MeSH subheadings.Artificial intelligence in medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Building high accuracy text classifiers is an important task in biomedicine given the wealth of information hidden in unstructured narratives such as research articles and clinical documents. Due to large feature spaces, traditionally, discriminative approaches such as logistic regression and support vector machines with n-gram and semantic features (e.g., named entities) have been used for text classification where additional performance gains are typically made through feature selection and ensemble approaches. In this paper, we demonstrate that a more direct approach using convolutional neural networks (CNNs) outperforms several traditional approaches in biomedical text classification with the specific use-case of assigning medical subject headings (or MeSH terms) to biomedical articles. Trained annotators at the national library of medicine (NLM) assign on an average 13 codes to each biomedical article, thus semantically indexing scientific literature to support NLM's PubMed search system. Recent evidence suggests that effective automated efforts for MeSH term assignment start with binary classifiers for each term. In this paper, we use CNNs to build binary text classifiers and achieve an absolute improvement of over 3% in macro F-score over a set of selected hard-to-classify MeSH terms when compared with the best prior results on a public dataset. Additional experiments on 50 high frequency terms in the dataset also show improvements with CNNs. Our results indicate the strong potential of CNNs in biomedical text classification tasks.
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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.