Victor M Murcia, Vinod Aggarwal, Nikhil Pesaladinne, Ram Thammineni, Nhan Do, Gil Alterovitz, Rafael B Fricks
{"title":"通过自然语言处理合成电子健康记录和临床试验资格标准实现临床试验匹配自动化。","authors":"Victor M Murcia, Vinod Aggarwal, Nikhil Pesaladinne, Ram Thammineni, Nhan Do, Gil Alterovitz, Rafael B Fricks","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Clinical trials are critical to many medical advances; however, recruiting patients remains a persistent obstacle. Automated clinical trial matching could expedite recruitment across all trial phases. We detail our initial efforts towards automating the matching process by linking realistic synthetic electronic health records to clinical trial eligibility criteria using natural language processing methods. We also demonstrate how the Sørensen-Dice Index can be adapted to quantify match quality between a patient and a clinical trial.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141802/pdf/","citationCount":"0","resultStr":"{\"title\":\"Automating Clinical Trial Matches Via Natural Language Processing of Synthetic Electronic Health Records and Clinical Trial Eligibility Criteria.\",\"authors\":\"Victor M Murcia, Vinod Aggarwal, Nikhil Pesaladinne, Ram Thammineni, Nhan Do, Gil Alterovitz, Rafael B Fricks\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Clinical trials are critical to many medical advances; however, recruiting patients remains a persistent obstacle. Automated clinical trial matching could expedite recruitment across all trial phases. We detail our initial efforts towards automating the matching process by linking realistic synthetic electronic health records to clinical trial eligibility criteria using natural language processing methods. We also demonstrate how the Sørensen-Dice Index can be adapted to quantify match quality between a patient and a clinical trial.</p>\",\"PeriodicalId\":72181,\"journal\":{\"name\":\"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141802/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Automating Clinical Trial Matches Via Natural Language Processing of Synthetic Electronic Health Records and Clinical Trial Eligibility Criteria.
Clinical trials are critical to many medical advances; however, recruiting patients remains a persistent obstacle. Automated clinical trial matching could expedite recruitment across all trial phases. We detail our initial efforts towards automating the matching process by linking realistic synthetic electronic health records to clinical trial eligibility criteria using natural language processing methods. We also demonstrate how the Sørensen-Dice Index can be adapted to quantify match quality between a patient and a clinical trial.