Deng Chen, ChengJie Lu, HongPeng Bai, Kaijian Xia, Meilian Zheng
{"title":"将人工智能与医疗产业链数据相结合:通过语义知识图谱加强临床营养研究。","authors":"Deng Chen, ChengJie Lu, HongPeng Bai, Kaijian Xia, Meilian Zheng","doi":"10.3389/fdgth.2024.1439113","DOIUrl":null,"url":null,"abstract":"<p><p>In clinical nutrition research, the medical industry chain generates a wealth of multidimensional spatial data across various formats, including text, images, and semi-structured tables. This data's inherent heterogeneity and diversity present significant challenges for processing and mining, which are further compounded by the data's diverse features, which are difficult to extract. To address these challenges, we propose an innovative integration of artificial intelligence (AI) with the medical industry chain data, focusing on constructing semantic knowledge graphs and extracting core features. These knowledge graphs are pivotal for efficiently acquiring insights from the vast and granular big data within the medical industry chain. Our study introduces the Clinical Feature Extraction Knowledge Mapping ( <math><mi>C</mi> <mi>F</mi> <mi>E</mi> <mi>K</mi> <mi>M</mi></math> ) model, designed to augment the attributes of medical industry chain knowledge graphs through an entity extraction method grounded in syntactic dependency rules. The <math><mi>C</mi> <mi>F</mi> <mi>E</mi> <mi>K</mi> <mi>M</mi></math> model is applied to real and large-scale datasets within the medical industry chain, demonstrating robust performance in relation extraction, data complementation, and feature extraction. It achieves superior results to several competitive baseline methods, highlighting its effectiveness in handling medical industry chain data complexities. By representing compact semantic knowledge in a structured knowledge graph, our model identifies knowledge gaps and enhances the decision-making process in clinical nutrition research.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484050/pdf/","citationCount":"0","resultStr":"{\"title\":\"Integrating AI with medical industry chain data: enhancing clinical nutrition research through semantic knowledge graphs.\",\"authors\":\"Deng Chen, ChengJie Lu, HongPeng Bai, Kaijian Xia, Meilian Zheng\",\"doi\":\"10.3389/fdgth.2024.1439113\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In clinical nutrition research, the medical industry chain generates a wealth of multidimensional spatial data across various formats, including text, images, and semi-structured tables. This data's inherent heterogeneity and diversity present significant challenges for processing and mining, which are further compounded by the data's diverse features, which are difficult to extract. To address these challenges, we propose an innovative integration of artificial intelligence (AI) with the medical industry chain data, focusing on constructing semantic knowledge graphs and extracting core features. These knowledge graphs are pivotal for efficiently acquiring insights from the vast and granular big data within the medical industry chain. Our study introduces the Clinical Feature Extraction Knowledge Mapping ( <math><mi>C</mi> <mi>F</mi> <mi>E</mi> <mi>K</mi> <mi>M</mi></math> ) model, designed to augment the attributes of medical industry chain knowledge graphs through an entity extraction method grounded in syntactic dependency rules. The <math><mi>C</mi> <mi>F</mi> <mi>E</mi> <mi>K</mi> <mi>M</mi></math> model is applied to real and large-scale datasets within the medical industry chain, demonstrating robust performance in relation extraction, data complementation, and feature extraction. It achieves superior results to several competitive baseline methods, highlighting its effectiveness in handling medical industry chain data complexities. By representing compact semantic knowledge in a structured knowledge graph, our model identifies knowledge gaps and enhances the decision-making process in clinical nutrition research.</p>\",\"PeriodicalId\":73078,\"journal\":{\"name\":\"Frontiers in digital health\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484050/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in digital health\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fdgth.2024.1439113\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in digital health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fdgth.2024.1439113","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
摘要
在临床营养研究中,医疗产业链产生了大量的多维空间数据,格式各异,包括文本、图像和半结构化表格。这些数据固有的异质性和多样性给数据的处理和挖掘带来了巨大挑战,而数据的多样化特征又进一步加剧了挑战的难度。为了应对这些挑战,我们提出了一种将人工智能(AI)与医疗产业链数据相结合的创新方法,重点是构建语义知识图谱和提取核心特征。这些知识图谱对于从医疗产业链中庞大而精细的大数据中有效获取洞察力至关重要。我们的研究引入了临床特征提取知识图谱(C F E K M)模型,旨在通过基于句法依赖规则的实体提取方法来增强医疗产业链知识图谱的属性。C F E K M 模型被应用于医疗产业链中的真实大规模数据集,在关系提取、数据补充和特征提取方面表现出色。与几种具有竞争力的基线方法相比,它取得了更优越的结果,凸显了它在处理医疗产业链数据复杂性方面的有效性。通过在结构化知识图谱中表示紧凑的语义知识,我们的模型可以识别知识差距,并增强临床营养研究的决策过程。
Integrating AI with medical industry chain data: enhancing clinical nutrition research through semantic knowledge graphs.
In clinical nutrition research, the medical industry chain generates a wealth of multidimensional spatial data across various formats, including text, images, and semi-structured tables. This data's inherent heterogeneity and diversity present significant challenges for processing and mining, which are further compounded by the data's diverse features, which are difficult to extract. To address these challenges, we propose an innovative integration of artificial intelligence (AI) with the medical industry chain data, focusing on constructing semantic knowledge graphs and extracting core features. These knowledge graphs are pivotal for efficiently acquiring insights from the vast and granular big data within the medical industry chain. Our study introduces the Clinical Feature Extraction Knowledge Mapping ( ) model, designed to augment the attributes of medical industry chain knowledge graphs through an entity extraction method grounded in syntactic dependency rules. The model is applied to real and large-scale datasets within the medical industry chain, demonstrating robust performance in relation extraction, data complementation, and feature extraction. It achieves superior results to several competitive baseline methods, highlighting its effectiveness in handling medical industry chain data complexities. By representing compact semantic knowledge in a structured knowledge graph, our model identifies knowledge gaps and enhances the decision-making process in clinical nutrition research.