| Challenge: | Recent studies have shown that structured domain knowledge can be used for textual inference tasks in the medical domain. |
| Approach: | They propose to integrate structured domain knowledge into a knowledge graph for the Medical NLI task. |
| Outcome: | The proposed approach improves the baseline BioELMo architecture for the Medical NLI task. |
Similar Papers
UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus (2021.naacl-main)
Copied to clipboard
| Challenge: | Contextual word embedding models do not take into account structured expert domain knowledge from a knowledge base. |
| Approach: | They propose a contextual embedding model that integrates domain knowledge during the pre-training process via a novel knowledge augmentation strategy. |
| Outcome: | The proposed model outperforms existing domain-specific models on common named-entity recognition (NER) and clinical natural language inference tasks. |
Lessons from Natural Language Inference in the Clinical Domain (D18-1)
Copied to clipboard
| Challenge: | State of the art models with deep neural networks lack generalization capabilities in specialized domains where training data is limited. |
| Approach: | They propose a dataset annotated by doctors performing a natural language inference task grounded in the medical history of patients. |
| Outcome: | The proposed model outperforms existing models in the clinical domain by incorporating domain knowledge from external data and lexical sources. |
MedWriter: Knowledge-Aware Medical Text Generation (2020.coling-main)
Copied to clipboard
Youcheng Pan, Qingcai Chen, Weihua Peng, Xiaolong Wang, Baotian Hu, Xin Liu, Junying Chen, Wenxiu Zhou
| Challenge: | Recent studies focus on the information of unstructured text rather than structured information of the knowledge graph. |
| Approach: | They propose a knowledge-aware text generation model for medical domains that incorporates knowledge graphs into the model to improve the quality of generated text. |
| Outcome: | The proposed model improves the quality of generated text and has robust superiority over other methods. |
Enhancing Biomedical Lay Summarisation with External Knowledge Graphs (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to lay summarisation are reliant on the source article, which is unlikely to include all the information necessary for a lay audience. |
| Approach: | They augment existing biomedical lay summarisation dataset with article-specific knowledge graphs that contain detailed information on relevant biomedically related concepts. |
| Outcome: | The proposed methods improve readability and explanation of technical concepts by integrating graph-based domain knowledge within lay summarisation models. |
Incorporating medical knowledge in BERT for clinical relation extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) are used for diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question/Answering. |
| Approach: | They propose to add medical knowledge to pre-trained language models to facilitate clinical relation extraction using a large text corpus. |
| Outcome: | The proposed model outperforms the state-of-the-art systems on the benchmark i2b2/VA 2010 clinical relation extraction dataset. |
Italian Word Embeddings for the Medical Domain (2024.lrec-main)
Copied to clipboard
| Challenge: | Neural word embeddings have proven valuable in the development of medical applications, but for the Italian language, there are no publicly available corpora, embedds, or evaluation resources tailored to this domain. |
| Approach: | They propose to use a corpus of medical texts to generate neural word embeddings in Italian using Metathesaurus concept graphs. |
| Outcome: | The results show that the new embeddings correlate well with human judgments regarding similarity and relatedness of medical concepts. |
medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs (2025.coling-main)
Copied to clipboard
| Challenge: | Electronic Medical Records (EMRs) are the digitized record of a patient's medical and health information and are integral to modern healthcare. |
| Approach: | They propose a framework that combines Large Language Models (LLMs) with knowledge graphs (KGs) to enhance diagnostic capabilities. |
| Outcome: | The proposed framework assigns weighted importance to entities in medical records based on their type, enabling precise localization of candidate diseases within KGs. |
Learning to Leverage High-Order Medical Knowledge Graph for Joint Entity and Relation Extraction (2023.findings-acl)
Copied to clipboard
| Challenge: | Medical terms are difficult to understand and relations between medical entities become complicated. |
| Approach: | They propose to leverage medical domain knowledge for extracting entities and relations for Chinese medical texts by building a heterogeneous graph based on medical knowledge graph. |
| Outcome: | The proposed method is more effective than state-of-the-art methods on real Chinese medical texts. |
Leveraging Knowledge Graph-Enhanced LLMs for Context-Aware Medical Consultation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in large language models have significantly influenced the field of online medical consultations, but critical challenges remain, such as the generation of hallucinated information and the integration of up-to-date medical knowledge. |
| Approach: | They propose a framework that combines retrieval-augmented generation with a structured medical knowledge graph. |
| Outcome: | The proposed framework outperforms baselines on two medical consultation datasets and shows significant improvements in hallucination reduction and clinical usefulness. |
MedicalSum: A Guided Clinical Abstractive Summarization Model for Generating Medical Reports from Patient-Doctor Conversations (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for summarizing medical conversations do not take clinical knowledge into account and are difficult to control. |
| Approach: | They propose a transformer-based sequence-to-sequence architecture for summarizing medical conversations by integrating medical domain knowledge from the Unified Medical Language System (UMLS). |
| Outcome: | The proposed model achieves state-of-the-art ROUGE score improvements of 0.8-2.1 points (including 6.2% error reduction in the PE section) it incorporates medical domain knowledge from the Unified Medical Language System (UMLS). |