Ruth Johnson, Uri Gottlieb, Galit Shaham, Lihi Eisen, Jacob Waxman, Stav Devons-Sberro, Curtis R Ginder, Peter Hong, Raheel Sayeed, Ben Y Reis, Ran D Balicer, Noa Dagan, Marinka Zitnik
{"title":"Unified Clinical Vocabulary Embeddings for Advancing Precision Medicine.","authors":"Ruth Johnson, Uri Gottlieb, Galit Shaham, Lihi Eisen, Jacob Waxman, Stav Devons-Sberro, Curtis R Ginder, Peter Hong, Raheel Sayeed, Ben Y Reis, Ran D Balicer, Noa Dagan, Marinka Zitnik","doi":"10.1101/2024.12.03.24318322","DOIUrl":null,"url":null,"abstract":"<p><p>Integrating clinical knowledge into AI remains challenging despite numerous medical guidelines and vocabularies. Medical codes, central to healthcare systems, often reflect operational patterns shaped by geographic factors, national policies, insurance frameworks, and physician practices rather than the precise representation of clinical knowledge. This disconnect hampers AI in representing clinical relationships, raising concerns about bias, transparency, and generalizability. Here, we developed a resource of 67,124 clinical vocabulary embeddings derived from a clinical knowledge graph tailored to electronic health record vocabularies, spanning over 1.3 million edges. Using graph transformer neural networks, we generated clinical vocabulary embeddings that provide a new representation of clinical knowledge by unifying seven medical vocabularies. These embeddings were validated through a phenotype risk score analysis involving 4.57 million patients from Clalit Healthcare Services, effectively stratifying individuals based on survival outcomes. Inter-institutional panels of clinicians evaluated the embeddings for alignment with clinical knowledge across 90 diseases and 3,000 clinical codes, confirming their robustness and transferability. This resource addresses gaps in integrating clinical vocabularies into AI models and training datasets, paving the way for knowledge-grounded population and patient-level models.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11643188/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv : the preprint server for health sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.12.03.24318322","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Integrating clinical knowledge into AI remains challenging despite numerous medical guidelines and vocabularies. Medical codes, central to healthcare systems, often reflect operational patterns shaped by geographic factors, national policies, insurance frameworks, and physician practices rather than the precise representation of clinical knowledge. This disconnect hampers AI in representing clinical relationships, raising concerns about bias, transparency, and generalizability. Here, we developed a resource of 67,124 clinical vocabulary embeddings derived from a clinical knowledge graph tailored to electronic health record vocabularies, spanning over 1.3 million edges. Using graph transformer neural networks, we generated clinical vocabulary embeddings that provide a new representation of clinical knowledge by unifying seven medical vocabularies. These embeddings were validated through a phenotype risk score analysis involving 4.57 million patients from Clalit Healthcare Services, effectively stratifying individuals based on survival outcomes. Inter-institutional panels of clinicians evaluated the embeddings for alignment with clinical knowledge across 90 diseases and 3,000 clinical codes, confirming their robustness and transferability. This resource addresses gaps in integrating clinical vocabularies into AI models and training datasets, paving the way for knowledge-grounded population and patient-level models.