Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases (2020.lrec-1)
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| 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. |
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Annotation of a Large Clinical Entity Corpus (D18-1)
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| Challenge: | Past researches have shown the superiority of statistical/ML approaches over the rule based approaches. |
| Approach: | They propose to annotate a clinical domain annotated corpus using a small data set or a narrower domain to take full advantage of machine learning. |
| Outcome: | The proposed corpus contains 5,160 clinical documents from forty different clinical specialties. |
Can Synthetic Text Help Clinical Named Entity Recognition? A Study of Electronic Health Records in French (2023.eacl-main)
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| 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. |
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A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature (P18-1)
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| Challenge: | In 2015 alone, about 100 manuscripts describing randomized controlled trials for medical interventions were published every day. |
| Approach: | They propose a corpus of 5,000 medical articles annotated with demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured. |
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A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)
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| 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 Entities in Medical Case Reports: Corpus and Experiments (2020.lrec-1)
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| Challenge: | Only very few annotated corpora in the medical domain exist. |
| Approach: | They propose to annotate medical entities in case reports from PubMed Central's open access library. |
| Outcome: | The proposed corpus is the first of its kind to be made available to the scientific community in English. |
GGPONC 2.0 - The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers (2022.lrec-1)
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Florian Borchert, Christina Lohr, Luise Modersohn, Jonas Witt, Thomas Langer, Markus Follmann, Matthias Gietzelt, Bert Arnrich, Udo Hahn, Matthieu-P. Schapranow
| Challenge: | despite advances in language resources, there is still a shortage of annotated corpora covering (German) medical language. |
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| Outcome: | The new corpus can be built upon clinical guidelines with reasonable coverage of medical terminology. |
Zero-shot Medical Entity Retrieval without Annotation: Learning From Rich Knowledge Graph Semantics (2021.findings-acl)
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| Challenge: | Current approaches to medical entity retrieval generalize poorly to unseen sub-specialties . zero-shot retrieval is challenging due to the high degree of ambiguity and variability in medical corpora . |
| Approach: | They propose a set of learning tasks designed to train efficient zero-shot entity retrieval models. |
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MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation (D19-3)
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| 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. |
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Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Framework (2025.naacl-long)
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| Challenge: | Recent studies have demonstrated that large language models (LLMs) can perform in named entity recognition tasks. |
| Approach: | They propose a framework for clinical named entity recognition that decomposes the entity recognition task into several retrievals of sub-types and then filters them. |
| Outcome: | The proposed framework improves on the clinical named entity recognition task. |
AnnoHID: LLM-Assisted Annotation Framework for Low-Resource Medical Texts (2026.acl-demo)
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Annisa Maulida Ningtyas, Guntur Budi Herwanto, Yunita Sari, Rifki Afina Putri, Filip Kovacevic, Alaa El-Ebshihy, Varvara Arzt, Florina Piroi
| Challenge: | Social media platforms are a popular way to communicate with medical experts and improve health literacy. |
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