Department of Computer Science & IT, The University of Lahore, Lahore, Pakistan.
Independent Researcher, Cosenza (CS), Italy.
Appl Clin Inform. 2021 Aug;12(4):910-923. doi: 10.1055/s-0041-1735180. Epub 2021 Sep 22.
Verbal autopsy is a technique used to collect information about a decedent from his/her family members using questionnaires, conducting interviews, making observations, and sampling. In substantial parts of the world, particularly in Africa and Asia, many deaths are unrecorded. In 2017, globally pregnant women were dying daily around 810 and 295,000 in a year because of pregnancy-related problems, pointed out by World Health Organization. Identifying the cause of a death is a complex process which requires in-depth medical knowledge and practical experience. Generally, medical practitioners possess different knowledge levels, set of abilities, and problem-solving skills. Additionally, the medical negligence plays a significant part in further worsening the situation. Accurate identification of the cause of death can help a government to take strategic measures to focus on, particularly increasing the death rate in a specific region.
This research provides a solution by introducing a semantic-based verbal autopsy framework for maternal death (SVAF-MD) to identify the cause of death. The proposed framework consists of four main components as follows: (1) clinical practice guidelines, (2) knowledge collection, (3) knowledge modeling, and (4) knowledge codification. Maternal ontology for the framework is developed using Protégé knowledge editor. Resource description framework application programming interface (API) for PHP (RAP) is used as a Semantic Web toolkit along with Simple Protocol and RDF Query Language (SPARQL) is used for querying with ontology to retrieve data.
The results show that 92% of maternal causes of deaths assigned using SVAF-MD correctly matched manual reports already prepared by gynecologists.
SVAF-MD, a semantic-based framework for the verbal autopsy of maternal deaths, assigns the cause of death with minimum involvement of medical practitioners. This research helps the government to ease down the verbal autopsy process, overcome the delays in reporting, and facilitate in terms of accurate results to devise the policies to reduce the maternal mortality.
尸检是一种通过问卷、访谈、观察和抽样等方式从死者家属那里收集死者信息的技术。在世界的许多地方，特别是在非洲和亚洲，有许多死亡没有记录。世界卫生组织指出，2017 年，全球每天有 810 名孕妇和 29.5 万名孕妇死于与妊娠相关的问题。确定死因是一个复杂的过程，需要深入的医学知识和实践经验。一般来说，医务人员具有不同的知识水平、能力和解决问题的能力。此外，医疗事故在进一步恶化这种情况方面起着重要作用。准确识别死因可以帮助政府采取战略措施，重点关注特定地区的死亡率上升问题。
本研究通过引入一个基于语义的产妇死亡尸检框架（SVAF-MD）来解决这个问题，以确定死因。该框架由四个主要组件组成：（1）临床实践指南，（2）知识收集，（3）知识建模，和（4）知识编码。该框架的产妇本体使用 Protégé 知识编辑器开发。使用 RAP 作为语义 Web 工具包，同时使用简单协议和 RDF 查询语言（SPARQL）对本体进行查询，以从数据中检索数据。
结果表明，使用 SVAF-MD 分配的 92%的产妇死因与已经由妇科医生准备好的手动报告相符。
SVAF-MD 是一个基于语义的产妇死亡尸检框架，它可以在最小程度上依赖医生的情况下确定死因。这项研究有助于政府简化尸检过程，减少报告延迟，并提供准确的结果，以便制定政策来降低产妇死亡率。
