|Place of Conferral||中国科学院自动化研究所|
In recent years, evidence-based public health decision-making has gradually become an important way of policy making in the field of public health because of its characteristic of relying on evidence. Especially since the outbreak of COVID-19, scientific research literature, news, social media and other text information related to COVID-19 have been constantly emerging, which has laid a broad evidence base for evidence-based public health decision-making. However, traditional evidence-based public health decision-making generally adopts manual screening of evidence. In the era of digital intelligence, faced with massive original evidence texts, it is necessary to adopt intelligent automation technology to excavate, extract and synthesize evidence. In this context, this paper explores evidence-based automatic knowledge mapping based on massive multi-source public health evidence information to provide support for public health management decisions. This research has important theoretical significance and application value for improving the efficiency of evidence acquisition in public health field and promoting the application of information extraction technology in evidence-based public health field.
Based on public health literature, microblog and epidemiological survey report, this paper carried out evidence-based knowledge graph. The main work and innovations of this paper are summarized as follows:
1. Evidence-based Knowledge Graph based on Public Health Literature
During COVID-19 outbreak, evaluating effectiveness of non-pharmaceutical interventions for epidemic prevention and control is very important. In order to solve this problem, researchers in the early outbreak have published a large number of modeling research literature. Such literature uses modeling studies to simulate the impact of non-pharmaceutical interventions in different countries and regions at different time periods. Under the condition of not having randomized controlled study, this kind of literature is seen as a reliable source of evidence of evidence-based public health decisions. Therefore, this article is based on the literature of the annotation and build discourse level information extraction data sets, and puts forward a kind of fusion based on the data set a semi-supervised learning algorithm of discourse level information extraction method. Based on an advanced multi-task information extraction model, this method can fully extract relevant information such as document-level entity relationship and coreference reference entity, and only need less annotation data to achieve high accuracy. Compared with the baseline model, the F1 score of entity extraction task proposed in this paper increased by 5.3% and that of relation extraction task increased by 4.5%. In addition, in order to support evidence-based public health decision-making, this paper presents two case studies, namely, a meta-analysis of the effectiveness evaluation of the lockdown and an evidence map of the effectiveness of non-drug interventions, to verify the effectiveness of the above information extraction method.
2. Evidence-based Knowledge Graph based on Public Health Microblog
After the implementation of epidemic prevention and control policies, text information on social media will reflect the results of the policies to a certain extent, which can serve as a reference for policy formulation and adjustment. To explore COVID - 19 during outbreak of a series of the impact of the epidemic prevention and control measures, this article is based on public health related weibo, annotation events, epidemic prevention and control policy and its results in the same sentence building causality extract data set. And we use a span-based entity relation extraction model for causal relation extraction. Experimental results show that although the model has a simple structure, its performance is superior, and it is suitable for this task scenario. Under the evaluation indexes that are precisely matched, the F1 scores of influencing prevention and control policies and the sub-types of prevention and control policies are increased by 0.04, 0.09 and 0.09 respectively. The F1 score of both prevention and control policy-impact and subtypes of prevention and control policy-impact increased by 0.11. A case study was conducted on the non-pharmaceutical intervention policies implemented in the early stage of COVID-19 in China. The analysis results showed that the evidence-based knowledge mapping could effectively present the relationship between prevention and control policies and their impact.
3. Evidence-based Knowledge Graph based on Epidemiological Survey Reports
The transmission relationship map of confirmed cases generated based on epidemiological investigation reports can not only effectively represent the risk of virus transmission, but also identify the infection path of each confirmed case, the possible infected population, and the types and characteristics of each epidemic. Therefore, it is of great significance for epidemic prevention and control, especially for cluster outbreaks. Since there is no specific design rule for the existing transmission relationship map of confirmed cases, and it is not suitable for evidence-based decision making, this paper designed a concise and clear transmission relationship map structure according to the cluster epidemic investigation guidelines, and used a rule-based information extraction method to automatically extract the attributes and social relationships of confirmed cases. A case study based on a large-scale inter-provincial transmission cluster showed that the map designed in this paper could effectively represent the transmission chain and play a positive role in epidemic prevention and control.
|杨芸榕. 面向公共卫生管理决策的知识图谱循证研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022.|
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