Increasing the efficiency of trial-patient matching: automated clinical trial eligibility pre-screening for pediatric oncology patients.
Increasing the efficiency of trial-patient matching: automated clinical trial eligibility pre-screening for pediatric oncology patients.
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DOI：
10.1186/s12911-015-0149-3
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发表时间：
2015-04-14
影响因子：
3.5
通讯作者：
Solti I
中科院分区：
文献类型：
--
作者：
Ni Y;Wright J;Perentesis J;Lingren T;Deleger L;Kaiser M;Kohane I;Solti I
临床试验的手动合格性筛选（ES）通常需要对患者记录进行劳动密集型审查，这需要使用许多资源。利用最先进的自然语言处理（NLP）和信息提取（IE）技术，我们试图提高医生在临床试验入组中的决策效率。为了显著减少工作人员筛选的潜在候选人，我们开发了一种自动ES算法来识别符合肿瘤学临床试验核心资格特征的患者。我们从ClinicalTrials.gov收集了2009年12月1日至2011年10月31日期间在我们机构积极招募肿瘤患者的55项临床试验的叙述性合格标准。同时，我们的ES算法从电子健康记录（EHR）数据字段中提取临床和人口统计信息，以代表同一时期接受癌症治疗的所有215名肿瘤患者的概况。然后，自动ES算法将试验标准与患者特征进行匹配，以识别潜在的试验患者匹配。在169例历史试验患者入组决策的参考集上验证匹配性能，并计算工作量、精确度、召回率、阴性预测值（NPV）和特异性。如果没有自动化，肿瘤学家平均需要审查每个试验163名患者，以复制每个试验的历史患者招募。当使用自动ES时，该工作量减少了85%至24名患者（精确度/召回率/NPV/特异性：12.6%/100.0%/100.0%/89.9%）。在没有自动化的情况下，肿瘤学家平均需要审查每位患者的42项试验，以复制回顾性数据集中发生的患者-试验匹配。使用自动ES，这一工作量减少了90%，只有四次试验（精确度/召回率/NPV/特异性：35.7%/100.0%/100.0%/95.5%）。通过利用NLP和IE技术，自动化ES可以显着提高肿瘤学家的试验筛查效率，并使通常被排除在试验招募之外的小型诊所能够参与。该算法有可能显着减少在癌症护理社区的新举措打算大大扩大试验访问和可用试验数量时执行临床研究的工作。本文的在线版本（doi：10.1186/s12911-015-0149-3）包含补充材料，可供授权用户使用。
Manual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Leveraging state-of-the-art natural language processing (NLP) and information extraction (IE) technologies, we sought to improve the efficiency of physician decision-making in clinical trial enrollment. In order to markedly reduce the pool of potential candidates for staff screening, we developed an automated ES algorithm to identify patients who meet core eligibility characteristics of an oncology clinical trial. We collected narrative eligibility criteria from ClinicalTrials.gov for 55 clinical trials actively enrolling oncology patients in our institution between 12/01/2009 and 10/31/2011. In parallel, our ES algorithm extracted clinical and demographic information from the Electronic Health Record (EHR) data fields to represent profiles of all 215 oncology patients admitted to cancer treatment during the same period. The automated ES algorithm then matched the trial criteria with the patient profiles to identify potential trial-patient matches. Matching performance was validated on a reference set of 169 historical trial-patient enrollment decisions, and workload, precision, recall, negative predictive value (NPV) and specificity were calculated. Without automation, an oncologist would need to review 163 patients per trial on average to replicate the historical patient enrollment for each trial. This workload is reduced by 85% to 24 patients when using automated ES (precision/recall/NPV/specificity: 12.6%/100.0%/100.0%/89.9%). Without automation, an oncologist would need to review 42 trials per patient on average to replicate the patient-trial matches that occur in the retrospective data set. With automated ES this workload is reduced by 90% to four trials (precision/recall/NPV/specificity: 35.7%/100.0%/100.0%/95.5%). By leveraging NLP and IE technologies, automated ES could dramatically increase the trial screening efficiency of oncologists and enable participation of small practices, which are often left out from trial enrollment. The algorithm has the potential to significantly reduce the effort to execute clinical research at a point in time when new initiatives of the cancer care community intend to greatly expand both the access to trials and the number of available trials. The online version of this article (doi:10.1186/s12911-015-0149-3) contains supplementary material, which is available to authorized users.
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影响因子：
3.5
作者：
Pressler TR;Yen PY;Ding J;Liu J;Embi PJ;Payne PR
通讯作者：
Payne PR
DOI：
10.1136/amiajnl-2012-001487
发表时间：
2013-09
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Li Q;Zhai H;Deleger L;Lingren T;Kaiser M;Stoutenborough L;Solti I
通讯作者：
Solti I
DOI：
10.1136/amiajnl-2014-002887
发表时间：
2015-01
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Ni Y;Kennebeck S;Dexheimer JW;McAneney CM;Tang H;Lingren T;Li Q;Zhai H;Solti I
通讯作者：
Solti I
影响因子：
2.2
作者：
Penberthy L;Brown R;Puma F;Dahman B
通讯作者：
Dahman B
影响因子：
--
作者：
Penberthy, Lynne T.;Dahman, Bassam A.;DeShazo, Jonathan P.
通讯作者：
DeShazo, Jonathan P.