Titlebook: Health Information Processing; 8th China Conference Buzhou Tang,Qingcai Chen,Haitian Wang Conference proceedings 2023 The Editor(s) (if app

脆弱吧

一夫一妻制

BG-INT: An Entity Alignment Interaction Model Based on BERT and GCN-source data includes monolingual and multilingual data. Entity alignment is a key technology for knowledge fusion, while existing entity alignment models only use entity part information to learn vector representations, which limits the performance of the models. This paper proposes an entity align

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An Semantic Similarity Matching Method for Chinese Medical Question Texte deep architecture BERT-MSBiLSTM-Attentions (BMA) which uses the Bidirectional Encoder Representations from Transformers (BERT), Multi-layer Siamese Bi-directional Long Short Term Memory (MSBiLSTM) and dual attention mechanism (Attentions) in order to solve the current question semantic similarity

走调

A Biomedical Named Entity Recognition Framework with Multi-granularity Prompt Tuningce. To address this challenge, this paper proposes Prompt-BioNER, a BioNER framework using prompt tuning. Specifically, the framework is based on multi-granularity prompt fusion and achieves different levels of feature extraction through masked language model and next sentence prediction pre-trained

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SUE

思想流动

流利圆滑

An End-to-End Knowledge Graph Based Question Answering Approach for COVID-19-based question answering, which is very valuable for biomedical domain. In addition, existing question answering methods rely on knowledge embedding models to represent knowledge (i.e., entities and questions), but the relations between entities are neglected. In this paper, we construct a COVID-19

机械

Discovering Combination Patterns of Traditional Chinese Medicine for the Treatment of Gouty Arthritia major challenge to explore effective drug combinations due to the complexity of TCM components and the wide variation in drug prescriptions. Data mining technology provides more accurate drug screening and disease prediction than classical statistical methods. This study explores the usage pattern

PACK

Automatic Classification of Nursing Adverse Events Using a Hybrid Neural Network Modelability to damage personal health or increase the economic burden of patients. At present, the analysis of nursing adverse event report mainly focuses on its structured report content. However, the unstructured text content in the report contains the whole process of the event, but it is often ignor