{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T13:15:28Z","timestamp":1758633328592},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2016,10,26]],"date-time":"2016-10-26T00:00:00Z","timestamp":1477440000000},"content-version":"vor","delay-in-days":182,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Objective (1) To develop an automated algorithm to predict a patient\u2019s response (ie, if the patient agrees or declines) before he\/she is approached for a clinical trial invitation; (2) to assess the algorithm performance and the predictors on real-world patient recruitment data for a diverse set of clinical trials in a pediatric emergency department; and (3) to identify directions for future studies in predicting patients\u2019 participation response.<\/jats:p><jats:p>Materials and Methods We collected 3345 patients\u2019 response to trial invitations on 18 clinical trials at one center that were actively enrolling patients between January 1, 2010 and December 31, 2012. In parallel, we retrospectively extracted demographic, socioeconomic, and clinical predictors from multiple sources to represent the patients\u2019 profiles. Leveraging machine learning methodology, the automated algorithms predicted participation response for individual patients and identified influential features associated with their decision-making. The performance was validated on the collection of actual patient response, where precision, recall, F-measure, and area under the ROC curve were assessed.<\/jats:p><jats:p>Results Compared to the random response predictor that simulated the current practice, the machine learning algorithms achieved significantly better performance (Precision\/Recall\/F-measure\/area under the ROC curve: 70.82%\/92.02%\/80.04%\/72.78% on 10-fold cross validation and 71.52%\/92.68%\/80.74%\/75.74% on the test set). By analyzing the significant features output by the algorithms, the study confirmed several literature findings and identified challenges that could be mitigated to optimize recruitment.<\/jats:p><jats:p>Conclusion By exploiting predictive variables from multiple sources, we demonstrated that machine learning algorithms have great potential in improving the effectiveness of the recruitment process by automatically predicting patients\u2019 participation response to trial invitations.<\/jats:p>","DOI":"10.1093\/jamia\/ocv216","type":"journal-article","created":{"date-parts":[[2016,4,28]],"date-time":"2016-04-28T16:33:59Z","timestamp":1461861239000},"page":"671-680","source":"Crossref","is-referenced-by-count":24,"title":["Will they participate? Predicting patients\u2019 response to clinical trial invitations in a pediatric emergency department"],"prefix":"10.1093","volume":"23","author":[{"given":"Yizhao","family":"Ni","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew F","family":"Beck","sequence":"additional","affiliation":[{"name":"Division of General and Community Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Regina","family":"Taylor","sequence":"additional","affiliation":[{"name":"Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jenna","family":"Dyas","sequence":"additional","affiliation":[{"name":"Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Imre","family":"Solti","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacqueline","family":"Grupp-Phelan","sequence":"additional","affiliation":[{"name":"Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Judith W","family":"Dexheimer","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA"},{"name":"Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2016,4,27]]},"reference":[{"issue":"4 Suppl","key":"2020110612352337000_ocv216-B1","doi-asserted-by":"crossref","first-page":"6S","DOI":"10.1016\/0197-2456(87)90004-3","article-title":"Recruitment experience in clinical trials: literature summary and annotated bibliography","volume":"8","author":"Hunninghake","year":"1987","journal-title":"Control Clin Trials."},{"issue":"3","key":"2020110612352337000_ocv216-B2","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.ygyno.2011.02.007","article-title":"Recruitment and retention results for a population-based cervical cancer biobehavioral clinical trial","volume":"121","author":"Osann","year":"2011","journal-title":"Gynecol Oncol."},{"issue":"Suppl 1","key":"2020110612352337000_ocv216-B3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.2106\/JBJS.L.00240","article-title":"The darker side of randomized trials: recruitment challenges","volume":"94","author":"Sibai","year":"2012","journal-title":"J Bone Joint Surg Am."},{"issue":"6","key":"2020110612352337000_ocv216-B4","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1177\/1740774512458992","article-title":"Barriers to therapeutic clinical trials enrollment: differences between African-American and white cancer patients identified at the time of eligibility assessment","volume":"9","author":"Penberthy","year":"2012","journal-title":"Clin Trials."},{"key":"2020110612352337000_ocv216-B5","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1186\/1756-0500-7-824","article-title":"What can we learn from a failed trial: insight into non-participation in a chat-based intervention trial for adolescents with psychosocial problems","volume":"7","author":"Crutzen","year":"2014","journal-title":"BMC Res Notes."},{"issue":"8","key":"2020110612352337000_ocv216-B6","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/1478-4505-13-8","article-title":"Barriers and opportunities for enhancing patient recruitment and retention in clinical research: findings from an interview study in an NHS academic health science centre","volume":"13","author":"Adams","year":"2015","journal-title":"Health Res Policy Syst."},{"issue":"13","key":"2020110612352337000_ocv216-B7","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1111\/j.1471-0528.2009.02355.x","article-title":"Colposuspension or TVT with anterior repair for urinary incontinence and prolapse: results of and lessons from a pilot randomised patient-preference study (CARPET 1)","volume":"116","author":"Tincello","year":"2009","journal-title":"BJOG."},{"issue":"1","key":"2020110612352337000_ocv216-B8","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1093\/fampra\/20.1.83","article-title":"How evidence based are recruitment strategies for randomized controlled trials in primary care? 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