非结构化临床叙述和信息融合对临床试验招募有多重要？
How essential are unstructured clinical narratives and information fusion to clinical trial recruitment?
摘要：
电子健康记录使用结构化的受控词汇表和非结构化的叙述性文本来捕获患者信息。虽然结构化数据通常编码实验室值，遇到和药物列表，但非结构化数据捕获医生对患者状况，预后和对治疗干预的反应的解释。在本文中，我们证明了从非结构化临床叙述中提取信息抽取对于大多数临床应用是必不可少的。我们进行了一项实证研究，以验证论点，并表明，结构化数据本身是不够的，在解决招募慢性淋巴细胞白血病（CLL）和前列腺癌的临床试验患者的资格标准。非结构化数据对于解决59%的CLL试验标准和77%的前列腺癌试验标准至关重要。更具体地说，为解决资格标准与时间的限制，我们需要时间推理和信息集成与医疗事件内和跨非结构化临床叙述和结构化数据。
英文摘要：
Electronic health records capture patient information using structured controlled vocabularies and unstructured narrative text. While structured data typically encodes lab values, encounters and medication lists, unstructured data captures the physician's interpretation of the patient's condition, prognosis, and response to therapeutic intervention. In this paper, we demonstrate that information extraction from unstructured clinical narratives is essential to most clinical applications. We perform an empirical study to validate the argument and show that structured data alone is insufficient in resolving eligibility criteria for recruiting patients onto clinical trials for chronic lymphocytic leukemia (CLL) and prostate cancer. Unstructured data is essential to solving 59% of the CLL trial criteria and 77% of the prostate cancer trial criteria. More specifically, for resolving eligibility criteria with temporal constraints, we show the need for temporal reasoning and information integration with medical events within and across unstructured clinical narratives and structured data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
