Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.
Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.
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DOI：
10.1136/amiajnl-2014-002887
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发表时间：
2015-01
期刊：
影响因子：
--
通讯作者：
Solti I
中科院分区：
文献类型：
--
作者：
Ni Y;Kennebeck S;Dexheimer JW;McAneney CM;Tang H;Lingren T;Li Q;Zhai H;Solti I
目的（1）开发一种用于城市三级护理儿科急诊科（艾德）临床试验的自动资格筛选（ES）方法;（2）评估自然语言处理（NLP）、信息提取（IE）和机器学习（ML）技术对真实世界临床数据和试验的有效性。数据和方法我们收集了2010年1月1日至2012年8月31日期间积极招募患者的13项随机选择的疾病特异性临床试验的合格标准。与此同时，我们回顾性地从电子健康记录（EHR）中选择了包括人口统计学、实验室数据和临床记录在内的数据字段，以代表同一时期访问艾德的所有202795例患者的资料。利用NLP、IE和ML技术，自动ES算法识别出符合试验标准的患者，以减少工作人员筛选的候选人。在医生生成的试验患者匹配的金标准和历史试验患者入组决策的参考标准上验证了性能，其中评估了工作量、平均平均精度（MAP）和召回率。结果与未自动化的情况相比，在金标准集上，自动化ES的工作量减少了92%，MAP为62.9%。自动化ES使试验筛选效率提高了450%。通过对试验患者匹配的参考集进行大规模评价，证实了金标准集的结果。讨论和结论通过利用试验标准的文本和EHR的内容，我们证明了基于NLP，IE和ML的自动ES可以成功地识别临床试验的患者。
Objectives (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials. Data and methods We collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period. Leveraging NLP, IE, and ML technologies, the automated ES algorithms identified patients whose profiles matched the trial criteria to reduce the pool of candidates for staff screening. The performance was validated on both a physician-generated gold standard of trial–patient matches and a reference standard of historical trial–patient enrollment decisions, where workload, mean average precision (MAP), and recall were assessed. Results Compared with the case without automation, the workload with automated ES was reduced by 92% on the gold standard set, with a MAP of 62.9%. The automated ES achieved a 450% increase in trial screening efficiency. The findings on the gold standard set were confirmed by large-scale evaluation on the reference set of trial–patient matches. Discussion and conclusion By exploiting the text of trial criteria and the content of EHRs, we demonstrated that NLP-, IE-, and ML-based automated ES could successfully identify patients for clinical trials.
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