{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:48:33Z","timestamp":1779256113849,"version":"3.51.4"},"reference-count":22,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"7","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clinical Cancer Informatics"],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:sec><jats:title>PURPOSE<\/jats:title><jats:p>Matching patients to clinical trials is cumbersome and costly. Attempts have been made to automate the matching process; however, most have used a trial-centric approach, which focuses on a single trial. In this study, we developed a patient-centric matching tool that matches patient-specific demographic and clinical information with free-text clinical trial inclusion and exclusion criteria extracted using natural language processing to return a list of relevant clinical trials ordered by the patient's likelihood of eligibility.<\/jats:p><\/jats:sec><jats:sec><jats:title>MATERIALS AND METHODS<\/jats:title><jats:p>Records from pediatric leukemia clinical trials were downloaded from ClinicalTrials.gov. Regular expressions were used to discretize and extract individual trial criteria. A multilabel support vector machine (SVM) was trained to classify sentence embeddings of criteria into relevant clinical categories. Labeled criteria were parsed using regular expressions to extract numbers, comparators, and relationships. In the validation phase, a patient-trial match score was generated for each trial and returned in the form of a ranked list for each patient.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>In total, 5,251 discretized criteria were extracted from 216 protocols. The most frequent criterion was previous chemotherapy\/biologics (17%). The multilabel SVM demonstrated a pooled accuracy of 75%. The text processing pipeline was able to automatically extract 68% of eligibility criteria rules, as compared with 80% in a manual version of the tool. Automated matching was accomplished in approximately 4 seconds, as compared with several hours using manual derivation.<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p>To our knowledge, this project represents the first open-source attempt to generate a patient-centric clinical trial matching tool. The tool demonstrated acceptable performance when compared with a manual version, and it has potential to save time and money when matching patients to trials.<\/jats:p><\/jats:sec>","DOI":"10.1200\/cci.23.00009","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T20:00:26Z","timestamp":1689019226000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":9,"title":["Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia"],"prefix":"10.1200","author":[{"given":"Samuel","family":"Kaskovich","sequence":"first","affiliation":[{"name":"Emergency Medicine Residency, Denver Health, Denver, CO"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3856-2055","authenticated-orcid":false,"given":"Kirk D.","family":"Wyatt","sequence":"additional","affiliation":[{"name":"Department of Pediatric Hematology\/Oncology, Roger Maris Cancer Center, Sanford Health, Fargo, ND"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1960-0562","authenticated-orcid":false,"given":"Tomasz","family":"Oliwa","sequence":"additional","affiliation":[{"name":"Center for Research Informatics, University of Chicago, Chicago, IL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5413-2541","authenticated-orcid":false,"given":"Luca","family":"Graglia","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of Chicago, Chicago, IL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7074-7247","authenticated-orcid":false,"given":"Brian","family":"Furner","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of Chicago, Chicago, IL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2955-3405","authenticated-orcid":false,"given":"Jooho","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of Chicago, Chicago, IL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3010-6960","authenticated-orcid":false,"given":"Anoop","family":"Mayampurath","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, WI"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-851X","authenticated-orcid":false,"given":"Samuel L.","family":"Volchenboum","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of Chicago, Chicago, IL"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"233","reference":[{"key":"b1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cct.2018.01.003"},{"key":"b2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2013.282542"},{"key":"b3","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2014.1160"},{"key":"b4","doi-asserted-by":"publisher","DOI":"10.1016\/j.tips.2019.05.005"},{"key":"b5","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-015-0149-3"},{"key":"b6","first-page":"46","volume":"2010","author":"Ross J","year":"2010","journal-title":"Summit Transl Bioinform"},{"key":"b7","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2014-002887"},{"key":"b8","volume":"36","author":"Helgeson J","year":"2018","journal-title":"J Clin Orthod"},{"key":"b9","doi-asserted-by":"publisher","DOI":"10.1093\/jamiaopen\/ooaa002"},{"key":"b15","doi-asserted-by":"publisher","DOI":"10.1007\/s43441-019-00030-4"},{"key":"b17","volume-title":"Clinical Trial Matching","year":"2020"},{"key":"b18","volume-title":"Foundation Medicine to Identify Patients Eligible for National Cancer Institute's NCI-MATCH (Molecular Analysis for Therapy Choice) Study"},{"key":"b22","author":"Mikolov T","journal-title":"arXiv"},{"key":"b23","doi-asserted-by":"publisher","DOI":"10.1080\/19312458.2018.1455817"},{"key":"b24","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"b25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2013.06.001"},{"key":"b27","doi-asserted-by":"publisher","DOI":"10.1200\/CCI.21.00075"},{"key":"b28","doi-asserted-by":"crossref","unstructured":"Platt JC: Probabilities for SV machines, in Smola AJ, Bartlett PL, Sch\u00f6lkopf B, Schuurmans D (eds): Advances in Large Margin Classifiers. 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