%0期刊文章%@ 1438-8871 %I JMIR出版物%V 24%卡塔尔世界杯8强波胆分析 N 8% P e39888 %T破译神经发育障碍中心理模式的多样性:使用自然语言处理的公共数据知识图表示%A Kaur,Manpreet %A Costello,Jeremy %A Willis,Elyse %A Kelm,Karen %A Reformat,Marek Z %A Bolduc,Francois V %+阿尔伯塔大学儿科系，3-020 Katz大楼，11315大道，埃德蒙顿，AB, T6G 2E1，加拿大，1780 492 9713，fbolduc@ualberta.ca %K概念图%K神经发育障碍%K知识图谱%K文本分析%K语义相关性%K PubMed %K论坛%K心智模型%D 2022 %7 5.8.2022 %9原创论文%J J医学互联网Res %G英语%X背景:了解个体如何思考一个话题，即心智模型，可以显著改善沟通，特别是在情绪和影响较高的医学领域。神经发育障碍(ndd)是一组诊断，影响全球18%的人口，涉及认知或社会功能发展的差异。在这项研究中，我们关注两种ndd，注意力缺陷多动障碍(ADHD)和自闭症谱系障碍(ASD)，它们涉及多种症状和干预措施，需要两个重要的利益相关者:父母和卫生专业人员之间的互动。对于每个利益相关者的心理模型之间的差异，我们的理解存在差距，这使得利益相关者之间的沟通变得更加困难。目的:我们的目标是从与每个利益相关者相关的基于网络的信息构建知识图(KGs)，作为心智模型的代理。这些kg将加速确定利益相关者之间共有的和不同的关注点。所开发的kg可以帮助改善ADHD和ASD患者的知识动员、沟通和护理。方法:我们通过收集网络论坛和PubMed文摘中与ADHD和ASD相关的文章创建了2个数据集。 We utilized the Unified Medical Language System (UMLS) to detect biomedical concepts and applied Positive Pointwise Mutual Information followed by truncated Singular Value Decomposition to obtain corpus-based concept embeddings for each data set. Each data set is represented as a KG using a property graph model. Semantic relatedness between concepts is calculated to rank the relation strength of concepts and stored in the KG as relation weights. UMLS disorder-relevant semantic types are used to provide additional categorical information about each concept’s domain. Results: The developed KGs contain concepts from both data sets, with node sizes representing the co-occurrence frequency of concepts and edge sizes representing relevance between concepts. ADHD- and ASD-related concepts from different semantic types shows diverse areas of concerns and complex needs of the conditions. KG identifies converging and diverging concepts between health professionals literature (PubMed) and parental concerns (web-based forums), which may correspond to the differences between mental models for each stakeholder. Conclusions: We show for the first time that generating KGs from web-based data can capture the complex needs of families dealing with ADHD or ASD. Moreover, we showed points of convergence between families and health professionals’ KGs. Natural language processing–based KG provides access to a large sample size, which is often a limiting factor for traditional in-person mental model mapping. Our work offers a high throughput access to mental model maps, which could be used for further in-person validation, knowledge mobilization projects, and basis for communication about potential blind spots from stakeholders in interactions about NDDs. Future research will be needed to identify how concepts could interact together differently for each stakeholder. %M 35930346 %R 10.2196/39888 %U //www.mybigtv.com/2022/8/e39888 %U https://doi.org/10.2196/39888 %U http://www.ncbi.nlm.nih.gov/pubmed/35930346