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Wang et al [10] gave a comprehensive survey of prompt engineering for health care NLP applications such as question-answering systems, text summarization, and machine translation. However, they did not compare and evaluate different types of prompts for specific clinical NLP tasks and how the performance varies across different LLMs.
JMIR Med Inform 2024;12:e55318
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Wang et al [21] represented each task using gradient information from a base model and trained an adaptation network that modulates a text classifier conditioned on the task representation.
There is only a recent study by Müller et al [22] that explored SNNs for FSL in NLP and demonstrated the high performance of pretrained SNNs that embed texts and labels. To the best of our knowledge, none of the studies referenced above are using SNNs to perform FSL in the clinical NLP domain.
JMIR AI 2023;2:e44293
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The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview
Wang et al [13] details how these methods were employed. We obtained 4.1 million GE sentence pairs and 1.1 million Epic sentence pairs. We randomly selected 1006 sentence pairs to be annotated by human experts. To ensure that no PHI existed in the final released data set, we manually removed PHI from each sentence. In the annotation phase, we asked 2 clinical experts to independently annotate each sentence pair in the Clinical STS data set on the basis of their semantic equivalence.
JMIR Med Inform 2020;8(11):e23375
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Wang et al made a comparison among clinical notes, biomedical literature, and their combination to test their performances with word embeddings [24]. Torii et al showed the performance for concept extraction using machine learning taggers across narratives from heterogeneous data sources [25]. A GOstruct extension was developed to annotate protein functions from heterogeneous data [26].
JMIR Med Inform 2018;6(4):e11301
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