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自动化系统从多种类型的日本临床文本捕获患者症状:回顾性研究

Tomohiro Nishiyama1, Ayane Yamaguchi2, Peitao Han1

  • 1Department of Information Science, Nara Institute of Science and Technology, Ikoma, Japan.

JMIR medical informatics
|September 24, 2024
PubMed
概括

这项研究开发了一种自然语言处理 (NLP) 系统,用于从多种电子健康记录文件类型中检测药物不良事件 (ADEs). 与单一文档分析相比,利用各种文件提高了ADE检测的准确性.

关键词:
欧洲人权理事会 欧洲人权理事会电子人权 (EHRs) 是一种电子人权.ML ML 在 ML在NLP中,我们使用了NLP.负面的负面影响药物不良反应 药物不良反应不良事件是不良事件.癌症 癌症 癌症 癌症 癌症发现 发现 发现 发现 发现检测到检测到的检测.检测 检测 检测 检测 检测毒品 毒品 毒品 是一种药物.药物 药物 药物 药物 药物 药物机器学习是机器学习.药物治疗 药物治疗药物 药物 药物 药物命名实体的认可 命名实体的认可自然语言处理自然语言处理.神经病变是一种神经病变.在这里,我们可以看到注意事项,注意事项.笔记 笔记 笔记 笔记 笔记瘤学 在瘤学方面.周边神经病变是一种神经病变.药物制造业的制药公司制药公司的药品.药品制品制品制品制品制品制品制品制品药品制药公司 药品制药公司药理学 药理学 药理学 药理学 药理学药物治疗药物治疗记录记录记录记录记录记录记录记录 记录 记录 记录 记录报告报告报告报告报告.报告报告报告报告报告报告.这是一个症状,一个症状.症状 症状 症状 症状 症状文本 文本 文本文本是文本,文本是文本.文本性 文本性 文本性

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