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The distribution of deaths by cause provides crucial information for public health planning, response, and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease…

应用统计 · 统计学 2018-11-28 Tsuyoshi Kunihama , Zehang Richard Li , Samuel J. Clark , Tyler H. McCormick

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…

应用统计 · 统计学 2023-05-01 Toshiya Yoshida , Trinity Shuxian Fan , Tyler McCormick , Zhenke Wu , Zehang Richard Li

In low-resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause-specific mortality fraction (CSMF) for populations.…

应用统计 · 统计学 2022-07-27 Kelly R. Moran , Elizabeth L. Turner , David Dunson , Amy H. Herring

Understanding cause-specific mortality rates is crucial for monitoring population health and designing public health interventions. Worldwide, two-thirds of deaths do not have a cause assigned. Verbal autopsy (VA) is a well-established tool…

统计方法学 · 统计学 2023-06-26 Zehang Richard Li , Zhenke Wu , Irena Chen , Samuel J. Clark

Cause-of-death data is fundamental for understanding population health trends and inequalities as well as designing and evaluating public health interventions. A significant proportion of global deaths, particularly in low- and…

应用统计 · 统计学 2025-02-04 Yu Zhu , Zehang Richard Li

In regions without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In such areas the majority of deaths occur outside hospitals and are not recorded.…

Determining causes of deaths (COD) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of…

统计方法学 · 统计学 2021-12-22 Zhenke Wu , Zehang Richard Li , Irena Chen , Mengbing Li

Learning dependence relationships among variables of mixed types provides insights in a variety of scientific settings and is a well-studied problem in statistics. Existing methods, however, typically rely on copious, high quality data to…

应用统计 · 统计学 2019-07-25 Zehang Richard Li , Tyler H. McCormick , Samuel J. Clark

A verbal autopsy (VA) consists of a survey with a relative or close contact of a person who has recently died. VA surveys are commonly used to infer likely causes of death for individuals when deaths happen outside of hospitals or…

应用统计 · 统计学 2018-11-16 Samuel J. Clark , Zehang Li , Tyler H. McCormick

Monitoring cause-of-death data is an important part of understanding disease burdens and effects of public health interventions. Verbal autopsy (VA) is a well-established method for gathering information about deaths outside of hospitals by…

统计方法学 · 统计学 2025-06-17 Yu Zhu , Zehang Richard Li

Verbal autopsy procedures are widely used for estimating cause-specific mortality in areas without medical death certification. Data on symptoms reported by caregivers along with the cause of death are collected from a medical facility, and…

统计方法学 · 统计学 2008-08-06 Gary King , Ying Lu

Verbal autopsy (VA) is a survey-based tool widely used to infer cause of death (COD) in regions without complete-coverage civil registration and vital statistics systems. In such settings, many deaths happen outside of medical facilities…

应用统计 · 统计学 2022-10-04 Zehang Richard Li , Jason Thomas , Eungang Choi , Tyler H. McCormick , Samuel J. Clark

The burden of disease is fundamental to understanding, prioritizing, and monitoring public health interventions. Cause of death is required to calculate the burden of disease, but in many parts of the developing world deaths are neither…

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…

其他统计学 · 统计学 2015-04-10 Samuel J. Clark , Tyler McCormick , Zehang Li , Jon Wakefield

Verbal autopsy (VA) is a critical tool for estimating causes of death in resource-limited settings where medical certification is unavailable. This study presents LA-VA, a proof-of-concept pipeline that combines Large Language Models (LLMs)…

计算与语言 · 计算机科学 2025-09-12 Yiqun T. Chen , Tyler H. McCormick , Li Liu , Abhirup Datta

The literature comparing the performance of algorithms for assigning cause of death using verbal autopsy data is fractious and does not reach a consensus on which algorithms perform best, or even how to do the comparison. This manuscript…

应用统计 · 统计学 2018-02-23 Samuel J. Clark

Lower-and-middle income countries are faced with challenges arising from a lack of data on cause of death (COD), which can limit decisions on population health and disease management. A verbal autopsy(VA) can provide information about a COD…

机器学习 · 计算机科学 2022-04-27 Thokozile Manaka , Terence Van Zyl , Alisha N Wade , Deepak Kar

Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsies) of a deceased individual. CCVA algorithms are typically trained on non-local data, then used to…

统计方法学 · 统计学 2019-04-26 Abhirup Datta , Jacob Fiksel , Agbessi Amouzou , Scott Zeger

A Verbal Autopsy is the record of an interview about the circumstances of an uncertified death. In developing countries, if a death occurs away from health facilities, a field-worker interviews a relative of the deceased about the…

计算与语言 · 计算机科学 2014-02-19 Samuel Danso , Eric Atwell , Owen Johnson

In settings where most deaths occur outside the healthcare system, verbal autopsies (VAs) are a common tool to monitor trends in causes of death (COD). VAs are interviews with a surviving caregiver or relative that are used to predict the…

计算与语言 · 计算机科学 2024-04-04 Shuxian Fan , Adam Visokay , Kentaro Hoffman , Stephen Salerno , Li Liu , Jeffrey T. Leek , Tyler H. McCormick
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