A comprehensive view of factors associated with AD/ADRD will significantly
aid in studies to develop new treatments for AD/ADRD and identify high-risk
populations and patients for prevention efforts. In our study, we summarized
the risk factors for AD/ADRD by reviewing existing meta-analyses and review
articles on risk and preventive factors for AD/ADRD. In total, we extracted 477
risk factors in 10 categories from 537 studies. We constructed an interactive
knowledge map to disseminate our study results. Most of the risk factors are
accessible from structured Electronic Health Records (EHRs), and clinical
narratives show promise as information sources. However, evaluating genomic
risk factors using RWD remains a challenge, as genetic testing for AD/ADRD is
still not a common practice and is poorly documented in both structured and
unstructured EHRs. Considering the constantly evolving research on AD/ADRD risk
factors, literature mining via NLP methods offers a solution to automatically
update our knowledge map.

通过回顾现有的荟萃分析和综述文章，本研究总结了 AD/ADRD 的风险因素，总共提取了 537 项研究中的 10 个类别的 477 个风险因素，并构建了一个交互式的知识图谱来传播研究结果。

在实际数据中识别与阿尔茨海默病及相关痴呆相关的因素的可行性

Feasibility of Identifying Factors Related to Alzheimer's Disease and  Related Dementia in Real-World Data

To address the challenge of identifying and understanding hidden dangers in
substations from unstructured text data, a novel dynamic analysis method is
proposed. This approach begins by analyzing and extracting data from the
unstructured text related to hidden dangers. It then leverages a flexible,
distributed data search engine built on Elastic-Search to handle this
information. Following this, the hidden Markov model is employed to train the
data within the engine. The Viterbi algorithm is integrated to decipher the
hidden state sequences, facilitating the segmentation and labeling of entities
related to hidden dangers. The final step involves using the Neo4j graph
database to dynamically create a knowledge map that visualizes hidden dangers
in the substation. This method's effectiveness is demonstrated through an
example analysis using data from a specific substation's hidden dangers.

研究提出了一种新的动态分析方法，通过从与潜在危险相关的非结构化文本中提取数据，利用基于 Elastic-Search 构建的灵活、分布式数据搜索引擎来处理该信息，并使用隐马尔科夫模型对引擎中的数据进行训练，整合维特比算法解析隐藏的状态序列，便于对与潜在危险相关的实体进行分割和标记，最后利用 Neo4j 图数据库动态创建可视化潜在危险的知识图谱，通过对特定变电站潜在危险数据的示例分析证明了该方法的有效性。

基于知识图谱的变电站隐患动态分析方法

Dynamic Analysis Method for Hidden Dangers in Substation Based on  Knowledge Graph

In recent years, data science has evolved significantly. Data analysis and
mining processes become routines in all sectors of the economy where datasets
are available. Vast data repositories have been collected, curated, stored, and
used for extracting knowledge. And this is becoming commonplace. Subsequently,
we extract a large amount of knowledge, either directly from the data or
through experts in the given domain. The challenge now is how to exploit all
this large amount of knowledge that is previously known for efficient
decision-making processes. Until recently, much of the knowledge gained through
a number of years of research is stored in static knowledge bases or
ontologies, while more diverse and dynamic knowledge acquired from data mining
studies is not centrally and consistently managed. In this research, we propose
a novel model called ontology-based knowledge map to represent and store the
results (knowledge) of data mining in crop farming to build, maintain, and
enrich the process of knowledge discovery. The proposed model consists of six
main sets: concepts, attributes, relations, transformations, instances, and
states. This model is dynamic and facilitates the access, updates, and
exploitation of the knowledge at any time. This paper also proposes an
architecture for handling this knowledge-based model. The system architecture
includes knowledge modelling, extraction, assessment, publishing, and
exploitation. This system has been implemented and used in agriculture for crop
management and monitoring. It is proven to be very effective and promising for
its extension to other domains.

该研究提出了基于本体的知识映射模型，以表示和存储作物种植的数据挖掘结果，以建立、维护和丰富知识发现过程，从而实现更高效的决策过程。

数字农业中的知识表达：迈向标准化模型的一步

Knowledge Representation in Digital Agriculture: A Step Towards Standardised Model

Normative expert systems have not become commonplace because they have been
difficult to build and use. Over the past decade, however, researchers have
developed the influence diagram, a graphical representation of a decision
maker's beliefs, alternatives, and preferences that serves as the knowledge
base of a normative expert system. Most people who have seen the representation
find it intuitive and easy to use. Consequently, the influence diagram has
overcome significantly the barriers to constructing normative expert systems.
Nevertheless, building influence diagrams is not practical for extremely large
and complex domains. In this book, I address the difficulties associated with
the construction of the probabilistic portion of an influence diagram, called a
knowledge map, belief network, or Bayesian network. I introduce two
representations that facilitate the generation of large knowledge maps. In
particular, I introduce the similarity network, a tool for building the network
structure of a knowledge map, and the partition, a tool for assessing the
probabilities associated with a knowledge map. I then use these representations
to build Pathfinder, a large normative expert system for the diagnosis of
lymph-node diseases (the domain contains over 60 diseases and over 100 disease
findings). In an early version of the system, I encoded the knowledge of the
expert using an erroneous assumption that all disease findings were
independent, given each disease. When the expert and I attempted to build a
more accurate knowledge map for the domain that would capture the dependencies
among the disease findings, we failed. Using a similarity network, however, we
built the knowledge-map structure for the entire domain in approximately 40
hours. Furthermore, the partition representation reduced the number of
probability assessments required by the expert from 75,000 to 14,000.

该研究论文介绍了影响图和知识地图的概念，并阐述了使用相似网络和分区表示的实用工具，以便更有效地构建大型具有复杂关系的知识地图，并讨论了在淋巴结诊断方面建立大型规范专家系统的实际可行性。