# Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1807.07425 · HTML: https://mlsanity.com/paper/1807.07425/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 22 of 22)

- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Consensus Graph Representation Learning for Better Grounded Image Captioning](https://mlsanity.com/paper/2112.00974/) (2021)
- [Textual Prompt Guided Image Restoration](https://mlsanity.com/paper/2312.06162/) (2023)
- [Relational Graph Learning for Grounded Video Description Generation](https://mlsanity.com/paper/2112.00967/) (2021)
- [Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domain](https://mlsanity.com/paper/2112.05807/) (2021)
- [TF-IDF vs Word Embeddings for Morbidity Identification in Clinical Notes: An Initial Study](https://mlsanity.com/paper/2105.09632/) (2021)
- [Text Classification of Cancer Clinical Trial Eligibility Criteria](https://mlsanity.com/paper/2309.07812/) (2023)
- [A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives](https://mlsanity.com/paper/2503.18213/) (2025)
- [Multimodal Machine Learning in Precision Health](https://mlsanity.com/paper/2204.04777/) (2022)
- [Ticket-BERT: Labeling Incident Management Tickets with Language Models](https://mlsanity.com/paper/2307.00108/) (2023)
- [Benchmarking Multilabel Topic Classification in the Kyrgyz Language](https://mlsanity.com/paper/2308.15952/) (2023)
- [Private Language Models via Truncated Laplacian Mechanism](https://mlsanity.com/paper/2410.08027/) (2024)
- [Cross-Lingual Knowledge Transfer for Clinical Phenotyping](https://mlsanity.com/paper/2208.01912/) (2022)
- [Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach](https://mlsanity.com/paper/2304.01240/) (2023)
- [Concept Extraction for Time Series with ECLAD-ts](https://mlsanity.com/paper/2504.05024/) (2025)
- [Improving Hospital Risk Prediction with Knowledge-Augmented Multimodal EHR Modeling](https://mlsanity.com/paper/2508.01970/) (2025)
- [AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury](https://mlsanity.com/paper/2205.03695/) (2022)
- [AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease](https://mlsanity.com/paper/2212.06042/) (2022)
- [A Study of Large Language Models for Patient Information Extraction: Model Architecture, Fine-Tuning Strategy, and Multi-task Instruction Tuning](https://mlsanity.com/paper/2509.04753/) (2025)
- [Meta-Path-Free Representation Learning on Heterogeneous Networks](https://mlsanity.com/paper/2102.08120/) (2021)
- [New Arabic Medical Dataset for Diseases Classification](https://mlsanity.com/paper/2106.15236/) (2021)
- [Investigating Alternative Feature Extraction Pipelines For Clinical Note Phenotyping](https://mlsanity.com/paper/2310.03772/) (2023)

## Abstract (arXiv)

Clinical text classification is an important problem in medical natural language processing. Existing studies have conventionally focused on rules or knowledge sources-based feature engineering, but only a few have exploited effective feature learning capability of deep learning methods. In this study, we propose a novel approach which combines rule-based features and knowledge-guided deep learning techniques for effective disease classification. Critical Steps of our method include identifying trigger phrases, predicting classes with very few examples using trigger phrases and training a convolutional neural network with word embeddings and Unified Medical Language System (UMLS) entity embeddings. We evaluated our method on the 2008 Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge. The results show that our method outperforms the state of the art methods.