MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation (D19-3)
Copied to clipboard
| Challenge: | 80% of biomedical data is stored in unstructured text such as electronic health records (EHRs). |
| Approach: | They propose a web-based interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model for biomedical domain text. |
| Outcome: | The proposed interface is designed to build, improve and customise a NER+L model for biomedical domain text and collate accurate research use case specific training data. |
Similar Papers
TrainX – Named Entity Linking with Active Sampling and Bi-Encoders (2020.coling-demos)
Copied to clipboard
Tom Oberhauser, Tim Bischoff, Karl Brendel, Maluna Menke, Tobias Klatt, Amy Siu, Felix Alexander Gers, Alexander Löser
| Challenge: | Existing easyto-use annotation tools do not support entity linking, which leads to additional training costs for medical professionals. |
| Approach: | They propose a system for Named Entity Linking for medical experts . they use Flair and BERT to support annotating training data with active sampling . |
| Outcome: | The proposed system is capable of linking against large knowledge bases and supporting zero-shot cases where the linker has never seen the entity before. |
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning (2022.naacl-main)
Copied to clipboard
| Challenge: | Generative methods for biomedical entity linking (EL) use synonyms knowledge from knowledge bases (KB) this is not trivial to inject into a generative method, but it is cost-effective. |
| Approach: | They propose to inject synonyms knowledge into a generative model of biomedical EL by constructing synthetic samples with synonyms and definitions from KB and requiring the model to recover concept names. |
| Outcome: | The proposed method achieves state-of-the-art results on several biomedical EL tasks without candidate selection. |
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. |
A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)
Copied to clipboard
| Challenge: | EHRs contain vast amounts of valuable clinical data, stored as unstructured text. |
| Approach: | They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task. |
| Outcome: | The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time. |
Named Entity Recognition for Chinese biomedical patents (2020.coling-main)
Copied to clipboard
| Challenge: | Existing attempts to address NER for Chinese biomedical texts have been limited due to the amount of Chinese biomedicine discoveries being patented. |
| Approach: | They train and evaluate Chinese biomedical patents NER models based on BERT . their model is optimized for Chinese bio-patent data and scored an F1 . |
| Outcome: | The proposed model achieves an F1 score of 0.540.15 for Chinese biomedical patent data. |
BioReddit: Word Embeddings for User-Generated Biomedical NLP (D19-62)
Copied to clipboard
| Challenge: | a corpus of medical-themed posts was scrapped from Reddit to train word embeddings on downstream tasks. |
| Approach: | They propose to train word embeddings from a corpus of medical forums from reddit scrapping posts from medical-themed subreddits. |
| Outcome: | The proposed system outperforms embeddings trained on general purpose data or on scientific papers when applied on user-generated content. |
Data-Constrained Synthesis of Training Data for De-Identification (2025.acl-long)
Copied to clipboard
| Challenge: | sensitive domains lack widely available datasets due to privacy risks . recent studies have focused on evaluating the privacy of the synthetic text . |
| Approach: | They domain-adapt LLMs to clinical domain and generate synthetic clinical texts . they then generate NER models that can be annotated with tags for PII . |
| Outcome: | The proposed model performs better than the original model using smaller datasets. |
Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases (2020.lrec-1)
Copied to clipboard
| Challenge: | Current annotation policies for medical corpora are not standardized across clinical texts of different types. |
| Approach: | They propose to annotate medical records of various types using a named entity recognition (NER) task. |
| Outcome: | The proposed annotation scheme is applicable to large-scale clinical NLP projects. |
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. |
OpenBioNER: Lightweight Open-Domain Biomedical Named Entity Recognition Through Entity Type Description (2025.findings-naacl)
Copied to clipboard
Alessio Cocchieri, Giacomo Frisoni, Marcos Martínez Galindo, Gianluca Moro, Giuseppe Tagliavini, Francesco Candoli
| Challenge: | Biomedical Named Entity Recognition (BioNER) is a computationally expensive and limited tool . specialized 7B NER LLMs and GPT-4o can't match textual spans with entity types . |
| Approach: | They propose a lightweight BERT-based cross-encoder architecture that can identify any biomedical entity using only its description. |
| Outcome: | The proposed system outperforms existing models that match textual spans with entity types rather than descriptions on biomedical benchmarks. |