Melissa J. Morine, C. Priami, Edith Coronado, Juliana Haber, J. Kaput
{"title":"A Comprehensive and Holistic Health Database","authors":"Melissa J. Morine, C. Priami, Edith Coronado, Juliana Haber, J. Kaput","doi":"10.1109/ICDH55609.2022.00039","DOIUrl":null,"url":null,"abstract":"Health and the initiation, progression, and outcome of disease are the result of multiple environmental factors interacting with individual genetic makeups. Collectively, results from primary clinical research on health and disease represent the most compendious and reliable source of actionable knowledge on strategies to optimize health. However, the dispersal of this information as unstructured data, distributed across millions of documents, is a substantial challenge in bridging the gap between primary research and concrete recommendations for improving health. Described here is the development and implementation of a machine reading pipeline that builds a knowledge graph of causal relationships between a broad range of predictive/modifiable diet and lifestyle factors and health outcomes, extracted from the vast biomedical corpus in the National Library of Medicine.","PeriodicalId":120923,"journal":{"name":"2022 IEEE International Conference on Digital Health (ICDH)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Digital Health (ICDH)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDH55609.2022.00039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Health and the initiation, progression, and outcome of disease are the result of multiple environmental factors interacting with individual genetic makeups. Collectively, results from primary clinical research on health and disease represent the most compendious and reliable source of actionable knowledge on strategies to optimize health. However, the dispersal of this information as unstructured data, distributed across millions of documents, is a substantial challenge in bridging the gap between primary research and concrete recommendations for improving health. Described here is the development and implementation of a machine reading pipeline that builds a knowledge graph of causal relationships between a broad range of predictive/modifiable diet and lifestyle factors and health outcomes, extracted from the vast biomedical corpus in the National Library of Medicine.