Deep Neural Models for Medical Concept Normalization in User-Generated Texts (P19-2)
Copied to clipboard
| Challenge: | a medical concept normalization problem is a challenge since social media texts are ambiguous and noisy . a recent study shows that neural architectures leverage the semantic meaning of the entity mention . |
| Approach: | They propose to map a health-related entity mention to a controlled vocabulary . they use powerful neural networks and contextualized word representation models . |
| Outcome: | The proposed model outperforms existing state-of-the-art models in mapping medical concepts to medical terms . the proposed model is based on recurrent neural networks and contextualized word representation models . |
Similar Papers
Clinical Concept Linking with Contextualized Neural Representations (2020.acl-main)
Copied to clipboard
| Challenge: | Entity linking systems rely on three sources of information: 1) similarity between mention string and entity name; 2) similarity of context of document to entity; 3) broader information about knowledge base; 4) contextual information; 5) semantic information; and 6) semantic information. |
| Approach: | They propose an approach to linking medical concepts to a medical concept ontology that leverages recent work in contextualized neural models. |
| Outcome: | The proposed approach outperforms a baseline approach and provides better initialization for the ranker. |
Biomedical Concept Normalization over Nested Entities with Partial UMLS Terminology in Russian (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing annotations in Russian do not include all entities, but only a small fraction of them are labeled in English. |
| Approach: | They present a manually annotated PubMed abstract dataset for concept normalization in Russian. |
| Outcome: | The proposed model improves on nested named entities in a zero-shot setting on bilingual terminology. |
Generalizing over Long Tail Concepts for Medical Term Normalization (2022.emnlp-main)
Copied to clipboard
Beatrice Portelli, Simone Scaboro, Enrico Santus, Hooman Sedghamiz, Emmanuele Chersoni, Giuseppe Serra
| Challenge: | Medical term normalization is a task of mapping a text to a large number of output classes. |
| Approach: | They propose a learning strategy that leverages hierarchical information to enhance generalizability of models. |
| Outcome: | The proposed strategy produces state-of-the-art performance on seen concepts and consistent improvements on unseen ones, allowing efficient zero-shot knowledge transfer across text typologies and datasets. |
Biomedical Concept Normalization by Leveraging Hypernyms (2021.emnlp-main)
Copied to clipboard
| Challenge: | Biomedical Concept Normalization (BCN) is widely used in biomedical text processing . despite numerous surface variants of biomedically-defined concepts, it remains challenging and unsolved. |
| Approach: | They propose a framework that uses hypernyms and synonyms to facilitate BCN . they use list-wise training to make use of both hypernies and synonym entities . |
| Outcome: | The proposed framework outperforms the state-of-the-art model on the NCBI dataset. |
A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization (2020.acl-main)
Copied to clipboard
| Challenge: | Concept normalization is a task that maps textual mentions of concepts to concepts in an ontology . lexical and grammatical variations are pervasive in such text, posing key challenges for data interoperability and the development of natural language processing (NLP) techniques. |
| Approach: | They propose a concept normalization framework that uses a candidate generator and a list-wise ranker to link concept mentions to concepts in an ontology. |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiple datasets. |
RuCCoN: Clinical Concept Normalization in Russian (2022.findings-acl)
Copied to clipboard
Alexandr Nesterov, Galina Zubkova, Zulfat Miftahutdinov, Vladimir Kokh, Elena Tutubalina, Artem Shelmanov, Anton Alekseev, Manvel Avetisian, Andrey Chertok, Sergey Nikolenko
| Challenge: | a new dataset for clinical concept normalization in Russian is available for download . ehrs contains over 16,028 entity mentions manually linked to over 2,409 unique concepts . |
| Approach: | They present a dataset for clinical concept normalization in Russian manually annotated by medical professionals. |
| Outcome: | The proposed dataset contains 16,028 entity mentions manually linked to over 2,409 unique concepts from the Russian language part of the UMLS ontology. |
A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization (2020.emnlp-main)
Copied to clipboard
| Challenge: | Medical entity normalization (NEN) is a task that links medical mentions to entities in knowledge bases. |
| Approach: | They propose a sequence generative framework to generate Chinese medical procedure entity normalization by constraint decoding and category-based model refining. |
| Outcome: | The proposed model improves on baselines especially in the case of multi-implication Chinese medical procedures. |
Neural Text Generation in Stories Using Entity Representations as Context (N18-1)
Copied to clipboard
| Challenge: | Existing models of text generation that explicitly represent entities are based on the use of words and entities. |
| Approach: | They propose a neural model that explicitly represents entities mentioned in the text . they use vectors that are updated as the text proceeds to improve automatic evaluations . |
| Outcome: | The proposed model improves mention generation, sentence selection, and sentence generation. |
Can Synthetic Text Help Clinical Named Entity Recognition? A Study of Electronic Health Records in French (2023.eacl-main)
Copied to clipboard
| Challenge: | In sensitive domains, the sharing of corpora is restricted due to confidentiality, copyrights or trade secrets. |
| Approach: | They use auto-regressive neural models to generate a clinical case corpus annotated with clinical entities and evaluate it for a named entity recognition task. |
| Outcome: | The proposed model can produce clinical case corpus annotated with clinical entities while maintaining confidentiality. |
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding (D18-1)
Copied to clipboard
| Challenge: | Existing methods for tagging unstructured texts with arbitrary number of terms drawn from an ontology are lacking. |
| Approach: | They propose a model for tagging unstructured texts with an arbitrary number of terms drawn from an ontology. |
| Outcome: | The proposed model yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts. |