Chinese medical information extraction
WebSep 10, 2024 · In this paper, a joint extraction model for Chinese electronic medical records is proposed, which can extract the risk factors of venous thrombosis from electronic medical records. Our proposed model achieves the best F1 score on the constructed data sets, and also achieves the fastest inference speed. WebFeb 14, 2024 · The largest schema-based Chinese medical information extraction dataset Medical Entity Extraction dataset (CMeIE) Footnote 1 is used for experiments . …
Chinese medical information extraction
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WebJul 12, 2024 · Named Entity Recognition (NER) is the initial step in extracting this knowledge from unstructured text and presenting it as a Knowledge Graph (KG). However, the previous approaches of NER have often suffered from small-scale human-labelled training data. Furthermore, extracting knowledge from Chinese medical literature is a … WebMar 8, 2024 · Medical information extraction consists of a group of natural language processing (NLP) tasks, which collaboratively convert clinical text to pre-defined structured formats. Current state-of-the-art (SOTA) NLP models are highly integrated with deep learning techniques and thus require massive annotated linguistic data. This study …
WebIn this paper, we present the Chinese Medical Information Extraction (CMeIE) dataset, consisting of 28, 008 sentences, 85, 282 triplets, 11 entities, and 44 relations derived from medical textbooks and clinical practices, constructed by several rounds of manual annotation.Additionally, we evaluate performances of the most recent state-of-the-art …
WebMar 2, 2024 · Medical entity extraction of RANs is an important task to get disease information for medical decision-making. For Chinese electronic medical records, … WebAug 14, 2024 · Medical relation extraction aims to automatically extract medical relations from the medical text for various medical researches. However, there are a few kinds of …
In this paper, we present the Chinese Medical Information Extraction (CMeIE) dataset, consisting of 28, 008 sentences, 85, 282 triplets, 11 entities, and 44 relations derived from medical textbooks and clinical practices, constructed by several rounds of manual annotation.Additionally, we evaluate … See more Due to the high density of triplets and overlapping entities in medical texts, we introduce the idea of probability graph [25] to design the training … See more In order to perform joint extraction of entity and relation task, we evaluate different frameworks: 1) Lattice LSTM [33] encoder with transition-based network. 2) CasRel [25] building upon different pre-trained encoders. Lattice … See more We investigate the effects of several popular pre-trained models as encoders of the hierarchical binary tagging framework. All pre-trained … See more
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