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首页> 美国卫生研究院文献>Journal of Biomedical Semantics >Combining string and phonetic similarity matching to identify misspelt names of drugs in medical records written in Portuguese
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Combining string and phonetic similarity matching to identify misspelt names of drugs in medical records written in Portuguese

机译：结合字符串和语音相似性匹配以识别葡萄牙语书写的医疗记录中药物的拼写错误名称

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There is a large amount of unstructured data being produced by different kinds of information systems, in a variety of formats, due to the advancement of communication and information technologies [ , ]. Within the clinical domain, Electronic Health Record (EHR) systems are becoming widely adopted, from which information describing the patient’s health conditions is often presented and stored in the form of free text notes [ ]. Existing text-mining methods aim to extract detailed structured information from clinical narratives, such as drug prescriptions, their variability, and adverse drug reactions [ , ]. However, free-text is susceptible to typing and phonetic misspellings. Spelling errors of generic drug names can occur in up to one out of six entries in electronic drug information systems. Such errors are likely to be responsible for up to 12% of adverse drug events, mainly caused by errors during transcription of prescriptions, illegible prescriptions, or drug name confusion [ ]. Due to such frequency and the relevance of drug information in clinical tasks, spelling correction becomes crucial to support health care professionals with spelling error-tolerant engine systems.
机译：由于通信和信息技术的发展，由各种信息系统以各种格式产生大量的非结构化数据。在临床领域中，电子健康记录（EHR）系统正在被广泛采用，从中经常以自由文本注释的形式呈现和存储描述患者健康状况的信息[]。现有的文本挖掘方法旨在从临床叙述中提取详细的结构化信息，例如药物处方，药物的变异性和药物不良反应[，]。但是，自由文本容易受到打字和拼写错误的影响。通用药品名称的拼写错误最多可能出现在电子药品信息系统中的六分之一中。此类错误可能导致多达12％的药品不良事件，这主要是由处方转录过程中的错误，处方难以理解或药品名称混淆[]引起的。由于这种频率和药物信息在临床任务中的相关性，拼写校正对于使用拼写容错引擎系统支持医疗保健专业人员变得至关重要。

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