ArticleJournal of the American Medical Informatics Association : JAMIA2019
ML-Net: multi-label classification of biomedical texts with deep neural networks.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- FLCMC: Federated Learning Approach for Chinese Medicinal Text Classification.Entropy (Basel, Switzerland) · 2024Article
- MLR-predictor: a versatile and efficient computational framework for multi-label requirements classification.Frontiers in artificial intelligence · 2024Article
- A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification.JMIR medical informatics · 2023Article
- Learning from undercoded clinical records for automated International Classification of Diseases (ICD) coding.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Automatic medical specialty classification based on patients' description of their symptoms.BMC medical informatics and decision making · 2023Article
- Environmental due diligence data: A novel corpus for training environmental domain NLP models.Data in brief · 2022Article
- LitCovid ensemble learning for COVID-19 multi-label classification.Database : the journal of biological databases and curation · 2022Article
- Deep learning for religious and continent-based toxic content detection and classification.Scientific reports · 2022Article
- Can Multi-Label Classifiers Help Identify Subjectivity? A Deep Learning Approach to Classifying Cognitive Presence in MOOCs.International journal of artificial intelligence in education · 2022Article
- A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data.PLoS computational biology · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
objectiveIn multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods, however, have only modest accuracy or efficiency and limited success in practical use. We propose ML-Net, a novel end-to-end deep learning framework, for multi-label classification of biomedical texts. MATERIALS AND
methodsML-Net combines a label prediction network with an automated label count prediction mechanism to provide an optimal set of labels. This is accomplished by leveraging both the predicted confidence score of each label and the deep contextual information (modeled by ELMo) in the target document. We evaluate ML-Net on 3 independent corpora in 2 text genres: biomedical literature and clinical notes. For evaluation, we use example-based measures, such as precision, recall, and the F measure. We also compare ML-Net with several competitive machine learning and deep learning baseline models.
resultsOur benchmarking results show that ML-Net compares favorably to state-of-the-art methods in multi-label classification of biomedical text. ML-Net is also shown to be robust when evaluated on different text genres in biomedicine.
conclusionML-Net is able to accuractely represent biomedical document context and dynamically estimate the label count in a more systematic and accurate manner. Unlike traditional machine learning methods, ML-Net does not require human effort for feature engineering and is a highly efficient and scalable approach to tasks with a large set of labels, so there is no need to build individual classifiers for each separate label.
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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.