Only about one-third of the deaths worldwide are assigned a medically-certified cause, and understanding the causes of deaths occurring outside of medical facilities is logistically and financially challenging. Verbal autopsy (VA) is a routinely used tool to collect information on cause of death in such settings. VA is a survey-based method where a structured questionnaire is conducted to family members or caregivers of a recently deceased person, and the collected information is used to infer the cause of death. As VA becomes an increasingly routine tool for cause-of-death data collection, the lengthy questionnaire has become a major challenge to the implementation and scale-up of VAs. In this paper, we propose a novel active questionnaire design approach that optimizes the order of the questions dynamically to achieve accurate cause-of-death assignment with the smallest number of questions. We propose a fully Bayesian strategy for adaptive question selection that is compatible with any existing probabilistic cause-of-death assignment methods. We also develop an early stopping criterion that fully accounts for the uncertainty in the model parameters. We also propose a penalized score to account for constraints and preferences of existing question structures. We evaluate the performance of our active designs using both synthetic and real data, demonstrating that the proposed strategy achieves accurate cause-of-death assignment using considerably fewer questions than the traditional static VA survey instruments.

翻译：全球仅有约三分之一的死亡病例获得了医学认证的死因，而在医疗机构外发生的死亡事件中，了解其死因在物流和财务上都极具挑战性。口头尸检（Verbal Autopsy, VA）是一种在此类场景中常规使用的死因信息收集工具。VA采用基于调查的方法，通过对近期逝者的家庭成员或照护者进行结构化问卷访谈，收集相关数据以推断死因。随着VA逐渐成为死因数据采集的常规手段，冗长的问卷已成为其推广和规模化应用的主要障碍。本文提出了一种新颖的主动式问卷设计方法，通过动态优化问题排序，以最少的问题数量实现准确的死因判定。我们提出了一种完全贝叶斯策略用于自适应问题选择，该方法可与任何现有概率性死因判定方法兼容。同时，我们开发了充分考虑模型参数不确定性的早期停止准则，并引入惩罚分数以应对现有问卷结构的约束与偏好。通过合成数据和真实数据的实验评估，本研究表明所提出的策略相比传统静态VA调查工具，能够使用显著更少的问题实现准确的死因判定。