New approaches to cohort selection.
New approaches to cohort selection.
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队列选择的新方法。
DOI：
10.1093/jamia/ocz174
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Uzuner,Özlem
中科院分区：
文献类型：
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作者：
Stubbs,Amber;Uzuner,Özlem
临床试验的队列选择是现代医学的重要组成部分，但它仍然是测试新治疗和干预措施中最困难，最耗时和最昂贵的方面之一。每项临床试验都定义了入选和排除标准，这些标准描述了试验所需的患者人群，以准确确定治疗的疗效。这些标准可以是宽泛的，仅限于特定的年龄或性别，或者可以是非常具体的，要求在一段时间内服用某些药物，或者患者的某些意图（即，怀孕的意图）。虽然一个简单的数据库搜索通常可以识别出合适年龄的患者，甚至是那些有特定诊断或测试结果的患者，更复杂的标准通常需要研究人员手动检查记录以识别合格的患者。或者，研究可能依赖于患者来寻找试验，或者由他们的医生指导试验的可能性，这两种情况都可能导致代表性偏见和误导性的试验结论。1，2对于我们的医学信息学领域，利用自然语言处理（NLP）工具来帮助识别患者意味着我们可以潜在地识别更多合格的患者，并从临床试验过程中消除自我选择的偏见，以及大大减少患者选择的时间和成本。《美国医学信息学协会杂志》的这期特刊探讨了NLP和机器学习如何帮助进行队列选择。
Cohort selection for clinical trials is a critical component of modern medicine, yet it remains one of the most difficult, time-consuming, and expensive aspects of testing new treatments and interventions. Each clinical trial defines inclusion and exclusion criteria that describe the required patient population for the trial to accurately determine efficacy of the treatment. These criteria can be broad, limited only to specific ages or genders, or can be very specific, requiring certain medications be taken in a time period, or certain intentions on the parts of the patients (ie, an intention to become pregnant).While a simple database search can often identify patients of the right age, or even those with particular diagnoses or test results, the more complex criteria often require study staff to manually examine records to identify qualified patients. Or, studies may rely on the patients to seek out the trial, or to be directed to trial possibilities by their doctors—both of which may lead to representation bias and misleading conclusions for the trial. 1, 2 For our field of medical informatics, leveraging natural language processing (NLP) tools to aid in patient identification means that we can, potentially, identify a larger number of qualified patients and remove the bias of self-selection from the clinical trial process, as well as greatly reduce the time and cost of patient selection. This special issue of Journal of the American Medical Informatics Association explores ways that NLP and machine learning can aid in cohort selection.