中文电子病历命名实体识别主要是研究电子病历病程记录文书数据集,文章提出对医疗手术麻醉文书数据集进行命名实体识别的研究。利用轻量级来自Transformer的双向编码器表示(A Lite Bidirectional Encoder Representation from Transform...中文电子病历命名实体识别主要是研究电子病历病程记录文书数据集,文章提出对医疗手术麻醉文书数据集进行命名实体识别的研究。利用轻量级来自Transformer的双向编码器表示(A Lite Bidirectional Encoder Representation from Transformers,ALBERT)预训练模型微调数据集和Tranfomers中的trainer训练器训练模型的方法,实现在医疗手术麻醉文书上识别手术麻醉事件命名实体与获取复杂麻醉医疗质量控制指标值。文章为医疗手术麻醉文书命名实体识别提供了可借鉴的思路,并且为计算复杂麻醉医疗质量控制指标值提供了一种新的解决方案。展开更多
Professor, Ph.D., Department Head Department of Civil Engineering, National Chung-Hsing University Tel： ＋886-4-22850989 E-mail： kjshou@dragon.nchu.edu.tw
针对现有的数字化档案多标签分类方法存在分类标签之间缺少关联性的问题,提出一种用于档案多标签分类的深层神经网络模型ALBERT-Seq2Seq-Attention.该模型通过ALBERT(A Little BERT)预训练语言模型内部多层双向的Transfomer结构获取进...针对现有的数字化档案多标签分类方法存在分类标签之间缺少关联性的问题,提出一种用于档案多标签分类的深层神经网络模型ALBERT-Seq2Seq-Attention.该模型通过ALBERT(A Little BERT)预训练语言模型内部多层双向的Transfomer结构获取进行文本特征向量的提取,并获得上下文语义信息;将预训练提取的文本特征作为Seq2Seq-Attention(Sequence to Sequence-Attention)模型的输入序列,构建标签字典以获取多标签间的关联关系.将分类模型在3种数据集上分别进行对比实验,结果表明:模型分类的效果F1值均超过90%.该模型不仅能提高档案文本的多标签分类效果,也能关注标签之间的相关关系.展开更多
Introduction: Seizures are one of the most common neurological complications in the infant period. The aim of our study was to describe the epidemiological, clinical, therapeutic and prognostic features of seizures in...Introduction: Seizures are one of the most common neurological complications in the infant period. The aim of our study was to describe the epidemiological, clinical, therapeutic and prognostic features of seizures in infants at the Albert Royer Children’s Hospital (Senegal). Materials and Methods: This was a retrospective, descriptive study from 1 January 2012 to 30 September 2018 of infants aged 0 days to 2 months who presented with seizures. Results: The hospital rate was 8.5%. Almost all the mothers (99.1%) had undergone at least 3 antenatal visits. Urogenital infection, gestational arterial hypertension and funicular anomalies were the main pregnancy-related pathologies. Delivery was vaginal in the majority of cases (80.9%). Most infants (43.6%) had not cried at birth. The majority of infants (63%) were born at term. Trophicity was normal in 68% of cases. The average age of the infants was 6.7 days. The main causes of seizures were hypoxic-ischemic encephalopathy (48.7%), metabolic disturbances (48.1%) and central ոеrvοսѕ system infections (15.6%). Phenobarbital was the 1st-line anticonvulsant. The case fatality rate was 39.5%. The main sequela observed were delayed psychomotor development (20.6%). Conclusion: Optimal management of infant seizures requires early diagnosis and etiological treatment by improving the quality of perinatal care to ensure better management of risk factors, as well as increasing the availability of neuroimaging equipment.展开更多
实体关系抽取任务是对句子中实体对间的语义关系进行识别。该文提出了一种基于Albert预训练语言模型结合图采样与聚合算法(Graph Sampling and Aggregation,GraphSAGE)的实体关系抽取方法,并在藏文实体关系抽取数据集上实验。该文针对...实体关系抽取任务是对句子中实体对间的语义关系进行识别。该文提出了一种基于Albert预训练语言模型结合图采样与聚合算法(Graph Sampling and Aggregation,GraphSAGE)的实体关系抽取方法,并在藏文实体关系抽取数据集上实验。该文针对藏文句子特征表示匮乏、传统藏文实体关系抽取模型准确率不高等问题,提出以下方案:①使用预先训练的藏文Albert模型获得高质量的藏文句子动态词向量特征;②使用提出的图结构数据构建与表示方法生成GraphSAGE模型的输入数据,并通过实验证明了该方法的有效性;③借鉴GraphSAGE模型的优势,利用其图采样与聚合操作进行关系抽取。实验结果表明,该文方法有效提高了藏文实体关系抽取模型的准确率,且优于基线实验效果。展开更多
文摘中文电子病历命名实体识别主要是研究电子病历病程记录文书数据集,文章提出对医疗手术麻醉文书数据集进行命名实体识别的研究。利用轻量级来自Transformer的双向编码器表示(A Lite Bidirectional Encoder Representation from Transformers,ALBERT)预训练模型微调数据集和Tranfomers中的trainer训练器训练模型的方法,实现在医疗手术麻醉文书上识别手术麻醉事件命名实体与获取复杂麻醉医疗质量控制指标值。文章为医疗手术麻醉文书命名实体识别提供了可借鉴的思路,并且为计算复杂麻醉医疗质量控制指标值提供了一种新的解决方案。
文摘Professor, Ph.D., Department Head Department of Civil Engineering, National Chung-Hsing University Tel： ＋886-4-22850989 E-mail： kjshou@dragon.nchu.edu.tw
文摘针对现有的数字化档案多标签分类方法存在分类标签之间缺少关联性的问题,提出一种用于档案多标签分类的深层神经网络模型ALBERT-Seq2Seq-Attention.该模型通过ALBERT(A Little BERT)预训练语言模型内部多层双向的Transfomer结构获取进行文本特征向量的提取,并获得上下文语义信息;将预训练提取的文本特征作为Seq2Seq-Attention(Sequence to Sequence-Attention)模型的输入序列,构建标签字典以获取多标签间的关联关系.将分类模型在3种数据集上分别进行对比实验,结果表明:模型分类的效果F1值均超过90%.该模型不仅能提高档案文本的多标签分类效果,也能关注标签之间的相关关系.
文摘Introduction: Seizures are one of the most common neurological complications in the infant period. The aim of our study was to describe the epidemiological, clinical, therapeutic and prognostic features of seizures in infants at the Albert Royer Children’s Hospital (Senegal). Materials and Methods: This was a retrospective, descriptive study from 1 January 2012 to 30 September 2018 of infants aged 0 days to 2 months who presented with seizures. Results: The hospital rate was 8.5%. Almost all the mothers (99.1%) had undergone at least 3 antenatal visits. Urogenital infection, gestational arterial hypertension and funicular anomalies were the main pregnancy-related pathologies. Delivery was vaginal in the majority of cases (80.9%). Most infants (43.6%) had not cried at birth. The majority of infants (63%) were born at term. Trophicity was normal in 68% of cases. The average age of the infants was 6.7 days. The main causes of seizures were hypoxic-ischemic encephalopathy (48.7%), metabolic disturbances (48.1%) and central ոеrvοսѕ system infections (15.6%). Phenobarbital was the 1st-line anticonvulsant. The case fatality rate was 39.5%. The main sequela observed were delayed psychomotor development (20.6%). Conclusion: Optimal management of infant seizures requires early diagnosis and etiological treatment by improving the quality of perinatal care to ensure better management of risk factors, as well as increasing the availability of neuroimaging equipment.
文摘实体关系抽取任务是对句子中实体对间的语义关系进行识别。该文提出了一种基于Albert预训练语言模型结合图采样与聚合算法(Graph Sampling and Aggregation,GraphSAGE)的实体关系抽取方法,并在藏文实体关系抽取数据集上实验。该文针对藏文句子特征表示匮乏、传统藏文实体关系抽取模型准确率不高等问题,提出以下方案:①使用预先训练的藏文Albert模型获得高质量的藏文句子动态词向量特征;②使用提出的图结构数据构建与表示方法生成GraphSAGE模型的输入数据,并通过实验证明了该方法的有效性;③借鉴GraphSAGE模型的优势,利用其图采样与聚合操作进行关系抽取。实验结果表明,该文方法有效提高了藏文实体关系抽取模型的准确率,且优于基线实验效果。
