| 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. |
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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. |
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. |
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. |
| Outcome: | The proposed corpus includes 5,000 medical articles describing clinical randomized controlled trials. |
The Medical Scribe: Corpus Development and Model Performance Analyses (2020.lrec-1)
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Izhak Shafran, Nan Du, Linh Tran, Amanda Perry, Lauren Keyes, Mark Knichel, Ashley Domin, Lei Huang, Yu-hui Chen, Gang Li, Mingqiu Wang, Laurent El Shafey, Hagen Soltau, Justin Stuart Paul
| Challenge: | Existing tools to assist in clinical note generation using audio of provider-patient encounters are lacking. |
| Approach: | They develop an annotation scheme to extract relevant clinical concepts from audio of provider-patient encounters and train a state-of-the-art tagging model. |
| Outcome: | The proposed model is more useful than the F-scores reflect and can be used in clinical notes. |
Medical Entity Corpus with PICO elements and Sentiment Analysis (L18-1)
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| Challenge: | In this paper, we establish a PICO and a sentiment annotated corpus of clinical trial publications. |
| Approach: | They propose to create a phrase-level PICO corpus and a sentence-level sentiment annotated corpus from clinical trial publications. |
| Outcome: | The proposed corpus is annotated on a phrase-level and a sentiment annotation on the same corpus. |
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. |
| Outcome: | The proposed model can produce clinical case corpus annotated with clinical entities while maintaining confidentiality. |
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. |
NeuroTrialNER: An Annotated Corpus for Neurological Diseases and Therapies in Clinical Trial Registries (2024.emnlp-main)
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Simona Doneva, Tilia Ellendorff, Beate Sick, Jean-Philippe Goldman, Amelia Cannon, Gerold Schneider, Benjamin Ineichen
| Challenge: | Despite substantial investment, developing new treatments for neurological conditions is a challenging and often unsuccessful endeavour. |
| Approach: | They propose a corpus for named entity recognition that is annotated clinical trial summaries from ClinicalTrials.gov. |
| Outcome: | The proposed corpus is annotated for neurological diseases, therapeutic interventions, and control treatments and achieves a close-to-human performance. |
Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition (P19-2)
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| Challenge: | Off-the-shelf word embeddings tend to perform poorly on texts from specialized domains such as clinical reports. |
| Approach: | They combine off-the-shelf contextual embeddings with static word2vec embedders trained on a small in-domain corpus built from task data to reach and sometimes outperform representations learned from a large corpus in the medical domain. |
| Outcome: | The proposed embedding strategies outperform representations learned from a large corpus in the medical domain. |
Exploring LLM Annotation for Adaptation of Clinical Information Extraction Models under Data-sharing Restrictions (2025.findings-acl)
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| Challenge: | In-hospital text data often contains valuable clinical information, yet fine-tuned small language models (SLMs) for information extraction remain challenging due to differences in formatting and vocabulary across institutions. |
| Approach: | They leverage large language models to annotate the target domain data for adaptation . they use in-hospital text data to extract clinical information . |
| Outcome: | The proposed model outperforms manual annotation on four clinical information extraction tasks with a larger number of annotated data. |