InSilicoVA：一种用于口头尸检的死因自动确定方法
InSilicoVA: A Method to Automate Cause of Death Assignment for Verbal Autopsy
摘要：
口头尸检（VA）被广泛用于提供发展中国家环境中的死因特异性死亡率估计，其中生命登记功能不佳。VA通过使用由护理人员提供的描述导致死亡的事件的信息来分配死亡原因。通常，医生阅读VA访谈并使用他们的专业知识分配原因。医生编码通常很慢，个别医生会给编码过程带来偏差，导致原因分配不可比。这些问题极大地限制了医生编码VA的实用性。解决这两个问题的一个方法是使用一种算法方法，将原因分配过程形式化。这确保了所分配的原因是可比的，并且需要更少的人-小时，以便可以在不干扰医生正常工作的情况下快速进行原因分配。Peter Byass的InterVA方法是VA编码最广泛使用的算法方法，与WHO 2012标准VA问卷一致。支持InterVA的统计模型可以改进;不确定性需要量化，人群水平的CSMF和个人水平的原因分配之间的联系需要在统计上严格。解决这些理论上的问题提供了一个机会，可以使用现代语言创建新的软件，可以在多个平台上运行，并将被广泛共享。在InterVA开创的整体框架的基础上，我们的工作创建了一个自动VA原因分配的统计模型。
英文摘要：
Verbal autopsies (VA) are widely used to provide cause-specific mortality estimates in developing world settings where vital registration does not function well. VAs assign cause(s) to a death by using information describing the events leading up to the death, provided by care givers. Typically physicians read VA interviews and assign causes using their expert knowledge. Physician coding is often slow, and individual physicians bring bias to the coding process that results in non-comparable cause assignments. These problems significantly limit the utility of physician-coded VAs. A solution to both is to use an algorithmic approach that formalizes the cause-assignment process. This ensures that assigned causes are comparable and requires many fewer person-hours so that cause assignment can be conducted quickly without disrupting the normal work of physicians. Peter Byass' InterVA method is the most widely used algorithmic approach to VA coding and is aligned with the WHO 2012 standard VA questionnaire. The statistical model underpinning InterVA can be improved; uncertainty needs to be quantified, and the link between the population-level CSMFs and the individual-level cause assignments needs to be statistically rigorous. Addressing these theoretical concerns provides an opportunity to create new software using modern languages that can run on multiple platforms and will be widely shared. Building on the overall framework pioneered by InterVA, our work creates a statistical model for automated VA cause assignment.
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