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美国佛罗里达州立大学信息学院何辙博士学术报告会
[ 来源: 信息学院 ] 2016/12/19 16:07:00

【题目】通过信息学方法优化临床试验病人选择标准设计(Optimizing Clinical Trial Participant Selection Design with Informatics)
【时间】2016年12月21日下午15:00
【地点】信息学院一楼报告厅
【摘要】

      临床试验通过在人体(病人或健康志愿者)进行药物系统性研究以证实或揭示试验药物的作用和安全性。在美国,临床前研究和临床试验通常会需要7到17年并耗资上亿美元。虽然临床试验普遍被认为现代医学研究的黄金标准,许多临床试验没有能很好地平衡内部效力和外部效力,导致它们的结果不具有真实世界病人的普遍适用性。即使药物通过了临床试验并得到美国食品药品监督管理局批准,当病人使用了这些药物后,可能出现了许多不良反应。西南肿瘤研究组(Southwest Oncology Group)发现,在新的癌症病例里大约有60%老年人, 但是在癌症临床研究里老年人只占了所有参与者的25%。过去有关临床试验人口代表性的研究通常方法是比较参与临床试验的病人和医院电子病例里的病人的区别,但是这种比较只能在临床试验完成之后进行。为了能更早的发现临床试验在病人选择标准设计上的问题,我们将ClinicalTrials.gov里的23万临床试验总结转化成包括试验元数据和细粒度选择标准变量的数据库。基于这个数据库,我们开发了一个基于Web的分析工具 VITTA (http://is.gd/VITTA)进行临床试验病人选择变量的分析。我们使用医院电子病历数据和美国国家营养健康调查数据量化了二型糖尿病和直肠结肠癌的临床试验的人口代表性。最近,我们开发了一个基于自然语言处理的工具对带有时间限制的定性病人合格标准的文本进行分析。本次讲座将介绍这些方法并说明数据驱动病人选择标准设计方法能够帮助临床试验设计者对其内部效力和外部效力进行平衡。

      Clinical studies are conducted for testing the efficacy and safety of a treatment (e.g., medication, device, and procedure) for one or more medical conditions. Drug development is expensive and time consuming. The preclinical and clinical trial phase may take 7-17 years and cost hundreds of millions of dollars. Even though clinical trials have been widely accepted as a gold standard of modern medical research, many of them failed to balance the internal validity and external validity, thereby limiting the applicability of the trial results to the real-world population. The lack of population representativeness is one of the major issues that lead to poor generalizability. A study of the Southwest Oncology Group (SWOG) reported that although about 60% of new cases of cancer occur among older adults, they only comprise 25% of participants in cancer clinical trials. Previously work on assessing the population representativeness of clinical trials often compares the enrolled patients with a sample of real-world patients, which can be only done after the completion of the trial. To enable early detection of the population representativeness issue using clinical trial eligibility criteria, we have transformed the clinical study summaries on ClinicalTrials.gov into discrete study metadata and eligibility criteria variables. We have built a web-based visual analytic tool named VITTA (http://is.gd/VITTA) to show how studies vary in their study populations with respect to study traits, one at a time. With a quantitative metric called Generalizability Index for Study Traits (GIST), we have assessed the population representativeness of type 2 diabetes trials and colorectal cancer treatment trials using patient data in a national survey and the electronic health records. We have further extended the GIST metric in a multivariate setting to allow investigators quantify the population representativeness of clinical studies with the joint use of multiple study traits simultaneously. Recently, we developed a free-text eligibility criteria parser to investigate the restrictiveness of qualitative eligibility criteria with temporal constraints. Informatics methods that leverage electronic data have the potential to facilitate data-driven optimization of clinical research participation selection design towards balanced internal validity and external validity.


【报告人】

      何辙, 现任美国佛罗里达州立大学(Florida State University) 信息学院助理教授 (Tenure-Track Assistant Professor)。2007年本科毕业于北京邮电大学计算机科学与技术学院,2009年硕士毕业于美国哥伦比亚大学计算机系,2009-2014年于美国新泽西理工学院计算机系从事医学本体及术语库(Biomedical Ontologies / Terminologies)质量控制的研究并于2014年1月获得博士学位 。2014– 2015于美国哥伦比亚大学医学信息学系全职担任博士后研究员(Postdoctoral Research Scientist),从事临床研究信息学(Clinical Research Informatics)的研究, 主要研究领域包括临床实验的设计和优化,医学术语库,文本挖掘,数据分析与挖掘。为IEEE, AMIA会员。担任Journal of Biomedical Informatics, Methods of Information in Medicine, PLOS One等SCI期刊及医学信息学领域旗舰会议AMIA Annual Symposium (美国医学信息学会年会),MEDINFO(世界医药信息学大会)的审稿人。曾担任多个生物医学信息学国际会议的联合主席及程序委员会委员。在包括Journal of Biomedical Informatics, Artificial Intelligence in Medicine, Methods of Information in Medicine等期刊及MEDINFO, AMIA Annual Symposium等医学信息领域国际主流学术期刊及会议上发表论文30于篇,其中SCI期刊论文10篇。发表的论文曾荣获美国医学信息学2015年会(AMIA 2015 Annual Symposium)最佳论文奖,入选医学信息学年度回顾 (Informatics Year in Review) 及临床研究信息学年度回顾 (Clinical Research Informatics Year in Review)。




该文发表于 2016/12/19 16:07:00 已被 xinxi 编辑 2016/12/19 16:34:00

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