A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries

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Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network (2015, background): “Therefore, many participating teams including all the top five systems used ML-based methods [12-14].”
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (2017, methodology): “In particular, the patient data contained in EHR systems has been used for such tasks as medical concept extraction [7], [8], patient trajectory modeling [9], disease inference [10], [11], clinical decision support systems [12], and more (Table I).”
Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries (2015, background): “CRF is the top choice for DNR since it is one of the most reliable sequence labeling algorithms and has shown good performances on a large number of NER tasks such as NER in the newswire domain [7,8], biomedical NER [9,10] and clinical NER [11,12].”
Large language models are few-shot clinical information extractors (2022, background): “The recognition of clinical concept mentions (problems, treatments, etc.), their modifiers (e.g., negation), and relations (e.g., dosage) is a fundamental building block in clinical NLP (Jiang et al., 2011).”
Improving large language models for clinical named entity recognition via prompt engineering (2024, unknown): “35 Regarding the GPT models, we used the specific versions GPT-3.”