ArticleBMC medical informatics and decision making2019
Clinical text classification with rule-based features and knowledge-guided convolutional neural networks.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.Blood advances · 2024Pooled it
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
- A study of large language models for patient information extraction: Model architecture, fine-tuning strategy, and multi-task instruction tuning.Journal of biomedical informatics · 2026Article
- Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications.JAMA network open · 2026Article
- An empirical evaluation of dimensionality reduction and class balancing for medical text classification.Scientific reports · 2025Article
- Early Diagnosis of Knee Osteoarthritis With a Natural Language Processing-Driven Approach Based on Clinician Notes: Development and Validation Study.JMIR formative research · 2025Article
- Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- [Medical text classification model integrating medical entity label semantics].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Exploring the full potential of the electronic health record: the application of natural language processing for clinical practice.European journal of cardiovascular nursing · 2025Article
- Entity-enhanced BERT for medical specialty prediction based on clinical questionnaire data.PloS one · 2025Article
- Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations.BMJ health & care informatics · 2024Article
- Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.Learning health systems · 2024Article
- A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history.BMC medical informatics and decision making · 2024Article
- The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review.Journal of medical systems · 2023Article
- Grad-CAM-Based Explainable Artificial Intelligence Related to Medical Text Processing.Bioengineering (Basel, Switzerland) · 2023Article
- AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Influence of online E-commerce interaction on consumer satisfaction based on big data algorithm.Heliyon · 2023Article
- Article
- A Disease-Prediction Protocol Integrating Triage Priority and BERT-Based Transfer Learning for Intelligent Triage.Bioengineering (Basel, Switzerland) · 2023Article
- A Systematic Review of Application Progress on Machine Learning-Based Natural Language Processing in Breast Cancer over the Past 5 Years.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundClinical text classification is an fundamental problem in medical natural language processing. Existing studies have cocnventionally focused on rules or knowledge sources-based feature engineering, but only a limited number of studies have exploited effective representation learning capability of deep learning methods.
methodsIn this study, we propose a new approach which combines rule-based features and knowledge-guided deep learning models for effective disease classification. Critical Steps of our method include recognizing trigger phrases, predicting classes with very few examples using trigger phrases and training a convolutional neural network (CNN) with word embeddings and Unified Medical Language System (UMLS) entity embeddings.
resultsWe evaluated our method on the 2008 Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge. The results demonstrate that our method outperforms the state-of-the-art methods.
conclusionWe showed that CNN model is powerful for learning effective hidden features, and CUIs embeddings are helpful for building clinical text representations. This shows integrating domain knowledge into CNN models is promising.
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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.