Increasing the efficiency of trial-patient matching: automated clinical trial eligibility Pre-screening for pediatric oncology patients
Open accessAbstract
BACKGROUND: Manual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Leveraging state-of-the-art natural language processing (NLP) and information extraction (IE) technologies, we sought to improve the efficiency of physician decision-making in clinical trial enrollment. In order to markedly reduce the pool of potential candidates for staff screening, we developed an automated ES algorithm to identify patients who meet core eligibility characteristics of an oncology clinical trial. METHODS: We collected narrative eligibility criteria from ClinicalTrials.gov for 55 clinical trials actively enrolling oncology patients in our institution between 12/01/2009 and 10/31/2011. In parallel, our ES algorithm extracted clinical and demographic information from the Electronic Health Record (EHR) data fields to represent profiles of all 215 oncology patients admitted to cancer treatment during the same period. The automated ES algorithm then matched the trial criteria with the patient profiles to identify potential trial-patient matches. Matching performance was validated on a reference set of 169 historical trial-patient enrollment decisions, and workload, precision, recall, negative predictive value (NPV) and specificity were calculated. RESULTS: Without automation, an oncologist would need to review 163 patients per trial on average to replicate the historical patient enrollment for each trial. This workload is reduced by 85% to 24 patients when using automated ES (precision/recall/NPV/specificity: 12.6%/100.0%/100.0%/89.9%). Without automation, an oncologist would need to review 42 trials per patient on average to replicate the patient-trial matches that occur in the retrospective data set. With automated ES this workload is reduced by 90% to four trials (precision/recall/NPV/specificity: 35.7%/100.0%/100.0%/95.5%). CONCLUSION: By leveraging NLP and IE technologies, automated ES could dramatically increase the trial screening efficiency of oncologists and enable participation of small practices, which are often left out from trial enrollment. The algorithm has the potential to significantly reduce the effort to execute clinical research at a point in time when new initiatives of the cancer care community intend to greatly expand both the access to trials and the number of available trials.
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BERT-based tools have been used for subject matching across various diseases while struggling with generalizability.
“Bidirectional Encoder Representations from Transformers (BERT)-based tools have been used for subject matching across various diseases while struggling with generalizability [4–7].”
From Enhancing hepatopathy clinical trial efficiency: a secure, large language model-powered pre-screening pipeline (2025) · cites this paper as [4]
The Methods (Automated ES algorithm; page unavailable) describe matching patients to clinical trials, but the tool uses cTAKES and term-based vectors, not BERT. The Discussion (Error analysis, limitations and future work; page unavailable) says the study was restricted to pediatric oncology and that transferability to other populations needs testing. It does not establish use across various diseases. (LLM, conf=0.88)
Experimental — accuracy not yet measuredTraditional manual screening and automated approaches fall short in key areas.
“While traditional manual screening and automated approaches have attempted to address these challenges, they fall short in key areas [4].”
From Enhancing hepatopathy clinical trial efficiency: a secure, large language model-powered pre-screening pipeline (2025) · cites this paper as [4]
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Cites
Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001560 · Cited by 1,967 · see citing works
A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries
Chapman, W. W., Bridewell, W. B., Hanbury, P., Cooper, G. F., & Buchanan, B. G. (2001) · Journal of Biomedical Informatics · doi:10.1006/jbin.2001.1029 · Cited by 1,121 · see citing works
Key factors in the rising cost of new drug discovery and development
Dickson, M., & Gagnon, J. P. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1382 · Cited by 501 · see citing works
Unlocking Clinical Data from Narrative Reports: A Study of Natural Language Processing
Hripcsak, G. M., Friedman, C., Alderson, P. O., DuMouchel, W., Johnson, S. B., & Clayton, P. D. (1995) · Annals of Internal Medicine · doi:10.7326/0003-4819-122-9-199505010-00007 · Cited by 340 · see citing works
Chapman, W. W., Nadkarni, P. M., Hirschman, L., D'Avolio, L. W., Savova, G. K., & Uzuner, O. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000465 · Cited by 296 · see citing works
Formal representation of eligibility criteria: A literature review
Weng, C., Tu, S. W., Sim, I., & Richesson, R. (2009) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2009.12.004 · Cited by 187 · see citing works
Effect of a Clinical Trial Alert System on Physician Participation in Trial Recruitment
Embí, P. J., Jain, A., Clark, J. R., Bizjack, S., Hornung, R., & Harris, C. M. (2005) · Archives of Internal Medicine · doi:10.1001/archinte.165.19.2272 · Cited by 183 · see citing works
Clinical research informatics: challenges, opportunities and definition for an emerging domain
Embi, P. J., & Payne, P. R. O. (2009) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m3005 · Cited by 163 · see citing works
Electronic Screening Improves Efficiency in Clinical Trial Recruitment
Thadani, S. R., Weng, C., Bigger, J. T., Ennever, J. F., & Wajngurt, D. (2009) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m3119 · Cited by 139 · see citing works
Ni, Y., Kennebeck, S., Dexheimer, J. W., McAneney, C. M., Tang, H., Lingren, T., Li, Q., Zhai, H., & Solti, I. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-002887 · Cited by 115 · see citing works
Self-citationEffort Required in Eligibility Screening for Clinical Trials
Penberthy, L. T., Dahman, B. A., Petkov, V. I., & DeShazo, J. P. (2012) · Journal of Oncology Practice · doi:10.1200/jop.2012.000646 · Cited by 96 · see citing works
Embí, P. J., Jain, A., Clark, J. A., & Harris, C. M. (2005) · Europe PMC (PubMed Central) · Cited by 90 · see citing works
Automated matching software for clinical trials eligibility: Measuring efficiency and flexibility
Penberthy, L., Brown, R., Puma, F., & Dahman, B. A. (2010) · Contemporary Clinical Trials · doi:10.1016/j.cct.2010.03.005 · Cited by 84 · see citing works
Embí, P. J., Jain, A. K., & Harris, C. M. (2008) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-8-13 · Cited by 66 · see citing works
Deléger, L., Brodzinski, H., Zhai, H., Li, Q., Lingren, T., Kirkendall, E. S., Alessandrini, E. A., & Solti, I. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001962 · Cited by 53 · see citing works
Self-citationEnrolling patients into clinical trials faster using RealTime Recuiting.
Butte, A. J., Weinstein, D. A., & Kohane, I. S. (2000) · PubMed Central · Cited by 51 · see citing works
Self-citationEdinger, T., Cohen, A. M., Bedrick, S. D., Ambert, K. H., & Hersh, W. (2012) · PubMed Central · Cited by 50 · see citing works
A Knowledge-Based Approach to Medical Records Retrieval.
Demner‐Fushman, D., Abhyankar, S., Yepes, A. J., Loane, R. F., Rance, B., Lang, F.-M., Ide, N. C., Apostolova, E., & Aronson, A. R. (2011) · Cited by 48 · see citing works
Pressler, T. R., Yen, P., Ding, J., Liu, J. H., Embí, P. J., & Payne, P. (2012) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-12-47 · Cited by 39 · see citing works
The accuracy and efficiency of electronic screening for recruitment into a clinical trial on COPD
Schmickl, C. N., Li, M., Li, G., Wetzstein, M. M., Herasevich, V., Gajic, O., & Benzo, R. P. (2011) · Respiratory Medicine · doi:10.1016/j.rmed.2011.04.012 · Cited by 37 · see citing works
Mining FDA drug labels for medical conditions
Li, Q., Deléger, L., Lingren, T., Zhai, H., Kaiser, M., Stoutenborough, L., Jegga, A. G., Cohen, K. B., & Solti, I. (2013) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-13-53 · Cited by 34 · see citing works
Self-citationResearch subject enrollment by primary care pediatricians using an electronic health record.
Grundmeier, R. W., Swietlik, M., & Bell, L. M. (2007) · PubMed · Cited by 33 · see citing works
Heinemann, S., Thüring, S., Wedeken, S., Schäfer, T., Scheidt‐Nave, C., Ketterer, M., & Himmel, W. (2011) · BMC Medical Research Methodology · doi:10.1186/1471-2288-11-16 · Cited by 30 · see citing works
Treweek, S. P., Pearson, E. R., Smith, N. K., Neville, R. G., Sargeant, P., Boswell, B., & Sullivan, F. (2010) · Journal of Innovation in Health Informatics · doi:10.14236/jhi.v18i1.753 · Cited by 28 · see citing works
Cengage Learning at TREC 2011 Medical Track
King, B., Wang, L., Provalov, I., & Zhou, J. (2011) · Text REtrieval Conference · Cited by 28 · see citing works
Sahoo, S. S., Tao, S., Parchman, A. J., Luo, J., Cui, L., Mergler, P., Lanese, R., Barnholtz‐Sloan, J. S., Meropol, N. J., & Zhang, G. (2014) · Cancer Informatics · doi:10.4137/cin.s19454 · Cited by 24 · see citing works
Limsopatham, N., McCreadie, R., Albakour, M., Macdonald, C., Santos, R. L. T., & Ounis, I. (2012) · Text REtrieval Conference · Cited by 21 · see citing works
Li, Q., Zhai, H., Deléger, L., Lingren, T., Kaiser, M., Stoutenborough, L., & Solti, I. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001487 · Cited by 21 · see citing works
Self-citationUniversity of Glasgow at Medical Records Track: Experiments with Terrier.
Limsopatham, N., Macdonald, C., Ounis, I., McDonald, G., & Bouamrane, M.-M. (2011) · Text REtrieval Conference · Cited by 20 · see citing works
A semantic platform for information retrieval from e-health records
Gurulingappa, H., Müller, B., Hofmann‐Apitius, M., & Fluck, J. (2011) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · Cited by 18 · see citing works
Exploring Evidence Aggregation Methods and External Expansion Sources for Medical Record Search.
Zhu, D., & Carterette, B. (2012) · Text REtrieval Conference · Cited by 15 · see citing works
Nkoy, F. L., Wolfe, D., Hales, J. W., Lattin, G., Rackham, M., & Maloney, C. G. (2009) · PubMed Central · Cited by 11 · see citing works
The design of a pre-encounter clinical trial screening tool: ASAP.
Ding, J., Erdal, S., Borlawsky, T. B., Liu, J., Golden‐Kreutz, D. M., Kamal, J., & Payne, P. (2008) · PubMed · Cited by 6 · see citing works
Supporting Patient Screening to Identify Suitable Clinical Trials
Bucur, A., Jasper, V. L., Chen, N.-Z., Claerhout, B., Kristof, D. S., Pérez-Rey, D., Alonso-Calvo, R., Pugliano, L., & Saini, K. S. (2014) · Studies in health technology and informatics · doi:10.3233/978-1-61499-432-9-823 · Cited by 6 · see citing works
Flagged first, then by citations
1 of the 35 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [29]Qi Y Laquerre PF Retrieving medical records with "sennamed": NEC labs America at TREC 2012 medical records track The twenty first Text REtrieval Conference Proceedings (TREC) 2012
- [33]Zhai H Iyer S Ni Y Lingren T Kirkendall E Li Q Mining a large-scale EHR with machine learning methods to predict all-cause 30-day unplanned readmissions Proc. of the 2nd ASE International Conference on Big Data Science and Computing 2014
Cited by
Sorted by citations
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review
Sheikhalishahi, S., Miotto, R., Dudley, J. T., Lavelli, A., Rinaldi, F., & Osmani, V. (2019) · JMIR Medical Informatics · doi:10.2196/12239 · Cited by 558 · see citing works
The Role of Artificial Intelligence in Early Cancer Diagnosis
Hunter, B., Hindocha, S., & Lee, R. W. (2022) · Cancers · doi:10.3390/cancers14061524 · Cited by 367 · see citing works
Evaluating eligibility criteria of oncology trials using real-world data and AI
Liu, R., Rizzo, S., Whipple, S., Pal, N., Pineda, A. L., Lu, M. W., Arnieri, B., Lü, Y., Capra, W. B., Copping, R., et al. (2021) · Nature · doi:10.1038/s41586-021-03430-5 · Cited by 326 · see citing works
Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress
Meystre, S. M., Lovis, C., Bürkle, T., Tognola, G., Budrionis, A., & Lehmann, C. U. (2017) · Yearbook of Medical Informatics · doi:10.15265/iy-2017-007 · Cited by 204 · see citing works
Automated Machine Learning for Healthcare and Clinical Notes Analysis
Mustafa, A., & Azghadi, M. R. (2021) · Computers · doi:10.3390/computers10020024 · Cited by 131 · see citing works
Beck, J. T., Rammage, M., Jackson, G. P., Preininger, A. M., Dankwa-Mullan, I., Roebuck, M. C., Torres, A., Holtzen, H., Coverdill, S. E., Williamson, M. P., et al. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00079 · Cited by 127 · see citing works
Frampton, G. K., Shepherd, J., Pickett, K., Griffiths, G., & Wyatt, J. C. (2020) · Trials · doi:10.1186/s13063-020-04358-3 · Cited by 119 · see citing works
Machine Learning Techniques for Biomedical Natural Language Processing: A Comprehensive Review
Houssein, E. H., Mohamed, R. E., & Ali, A. A. (2021) · IEEE Access · doi:10.1109/access.2021.3119621 · Cited by 114 · see citing works
Alexander, M., Solomon, B., Ball, D., Sheerin, M., Dankwa‐Mullan, I., Preininger, A. M., Jackson, G. P., & Herath, D. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa002 · Cited by 83 · see citing works
Lu, X., Yang, C., Liang, L., Hu, G., Zhong, Z., & Jiang, Z. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae243 · Cited by 77 · see citing works
“Benefits of AI tools Increasing efficiency, cost savings, and improving recruitment 1–6,8, Improving accuracy and predictive power 1,3–6,8–13,15,17,20,22,23,25,26,28,32–34, 36,42,44,46 Create user-friendly interfaces and improve patient satisfaction 43,45 Allow remote access and monitoring 13,40”
BackgroundCohort selection for clinical trials: n2c2 2018 shared task track 1
Stubbs, A., Filannino, M., Soysal, E., Henry, S., & Uzuner, Ö. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz163 · Cited by 73 · see citing works
Automatic trial eligibility surveillance based on unstructured clinical data
Meystre, S. M., Heider, P. M., Kim, Y.-J., Aruch, D. B., & Britten, C. D. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.05.018 · Cited by 71 · see citing works
Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer
Bitterman, D. S., Miller, T. A., Mak, R. H., & Savova, G. (2021) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2021.01.044 · Cited by 68 · see citing works
Chow, R., Midroni, J., Kaur, J., Boldt, G., Liu, G., Eng, L., Liu, F.-F., Haibe-Kains, B., Lock, M., & Raman, S. (2023) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/djad013 · Cited by 65 · see citing works
Artificial Intelligence for Perioperative Medicine: Perioperative Intelligence
Maheshwari, K., Cywinski, J. B., Papay, F., Khanna, A. K., & Mathur, P. (2022) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000005952 · Cited by 64 · see citing works
Tissot, H. C., Shah, A. D., Brealey, D., Harris, S., Agbakoba, R., Folarin, A., Romao, L., Roguski, L., Dobson, R., & Asselbergs, F. W. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2977925 · Cited by 61 · see citing works
Ni, Y., Bermudez, M., Kennebeck, S., Liddy-Hicks, S., & Dexheimer, J. W. (2019) · JMIR Medical Informatics · doi:10.2196/14185 · Cited by 60 · see citing works
Self-citationUsing automatically extracted information from mammography reports for decision-support
Bozkurt, S., Gimenez, F., Burnside, E. S., Gülkesen, K. H., & Rubin, D. L. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.07.001 · Cited by 59 · see citing works
Simmons, M., Singhal, A., & Lu, Z. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-981-10-1503-8_7 · Cited by 59 · see citing works
Zhang, K., & Demner‐Fushman, D. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw176 · Cited by 55 · see citing works
Idnay, B., Dreisbach, C., Weng, C., & Schnall, R. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab228 · Cited by 54 · see citing works
Wang, L., Fu, S., Wen, A., Ruan, X., He, H., Liu, S., Moon, S., Mai, M., Riaz, I. B., Wang, N., et al. (2022) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00006 · Cited by 53 · see citing works
Matching patients to clinical trials using semantically enriched document representation
Hassanzadeh, H., Karimi, S., & Nguyen, A. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103406 · Cited by 52 · see citing works
The role of artificial intelligence in hastening time to recruitment in clinical trials
Ismail, A., Al-Zoubi, T., Naqa, I. E., & Saeed, H. (2023) · BJR|Open · doi:10.1259/bjro.20220023 · Cited by 47 · see citing works
Rogers, J. R., Lee, J., Zhou, Z., Cheung, Y. K., Hripcsak, G. M., & Weng, C. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa224 · Cited by 46 · see citing works
Cai, T. A., Cai, F., Dahal, K. P., Cremone, G., Lam, E., Golnik, C., Seyok, T., Hong, C., Cai, T., & Liao, K. P. (2021) · ACR Open Rheumatology · doi:10.1002/acr2.11289 · Cited by 44 · see citing works
Automated radiology report summarization using an open-source natural language processing pipeline
Goff, D. J., & Loehfelm, T. W. (2018) · Journal of Digital Imaging · doi:10.1007/s10278-017-0030-2 · Cited by 43 · see citing works
Landschaft, A., Antweiler, D., Mackay, S., Kugler, S., Rüping, S., Wrobel, S., Höres, T., & Allende-Cid, H. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105531 · Cited by 41 · see citing works
Ni, Y., Barzman, D. H., Bachtel, A., Griffey, M., Osborn, A. J., & Sorter, M. T. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104137 · Cited by 41 · see citing works
Self-citationWilliams, E., Kienast, M., Medawar, E., Reinelt, J. D., Merola, A., Klopfenstein, S. A. I., Flint, A. R., Heeren, P., Poncette, A.-S., Balzer, F., et al. (2023) · JMIR Medical Informatics · doi:10.2196/43847 · Cited by 38 · see citing works
Application of Information Technology to Clinical Trial Evaluation and Enrollment: A Review
von Itzstein, M. S., Hullings, M., Mayo, H., Beg, M. S., Williams, E. L., & Gerber, D. E. (2021) · JAMA Oncology · doi:10.1001/jamaoncol.2021.1165 · Cited by 36 · see citing works
Ni, Y., Bachtel, A., Nause, K., & Beal, S. J. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab116 · Cited by 33 · see citing works
Self-citationThe Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources
Mathé, E. A., Hays, J. L., Stover, D. G., & Chen, J. L. (2018) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1667085 · Cited by 32 · see citing works
A review of research on eligibility criteria for clinical trials
Su, Q., Cheng, G., & Huang, J. (2023) · Clinical and Experimental Medicine · doi:10.1007/s10238-022-00975-1 · Cited by 32 · see citing works
A call to action to advance patient‐focused and decentralized clinical trials
Harvey, R. D., Miller, T. M., Hurley, P. A., Thota, R., Black, L. J., Bruinooge, S. S., Boehmer, L., Fleury, M. E., Kamboj, J., Rizvi, M. A., et al. (2024) · Cancer · doi:10.1002/cncr.35145 · Cited by 31 · see citing works
Beattie, J., Neufeld, S., Yang, D., Chukwuma, C., Gul, A., Desai, N., Jiang, S., & Dohopolski, M. (2024) · Cureus · doi:10.7759/cureus.60044 · Cited by 30 · see citing works
Meystre, S. M., Heider, P. M., Cates, A., Bastian, G., Pittman, T., Gentilin, S. M., & Kelechi, T. J. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-01916-6 · Cited by 30 · see citing works
Automated eligibility screening and monitoring for genotype-driven precision oncology trials
Eubank, M. H., Hyman, D. M., Kanakamedala, A., Gardos, S. M., Wills, J., & Stetson, P. D. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw020 · Cited by 30 · see citing works
Combining human and machine intelligence for clinical trial eligibility querying
Fang, Y., Idnay, B., Sun, Y., Liu, H., Chen, Z., Marder, K. S., Xu, H., Schnall, R., & Weng, C. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac051 · Cited by 29 · see citing works
Evolving Role and Future Directions of Natural Language Processing in Gastroenterology
Nehme, F., & Feldman, K. (2020) · Digestive Diseases and Sciences · doi:10.1007/s10620-020-06156-y · Cited by 28 · see citing works
O'Brien, E. C., Raman, S. R., Ellis, A. M., Hammill, B. G., Berdan, L. G., Rorick, T. L., Janmohamed, S. G., Lampron, Z. M., Hernandez, A. F., & Curtis, L. H. (2021) · Trials · doi:10.1186/s13063-021-05397-0 · Cited by 28 · see citing works
Sinnott, C., Georgiadis, A., Park, J., & Dixon‐Woods, M. (2020) · The Annals of Family Medicine · doi:10.1370/afm.2485 · Cited by 26 · see citing works
Learning Eligibility in Cancer Clinical Trials Using Deep Neural Networks
Bustos, A., & Pertusa, A. (2018) · Applied Sciences · doi:10.3390/app8071206 · Cited by 26 · see citing works
Artificial intelligence in oncology
Bibault, J., Burgun, A., Fournier, L. S., Dekker, A. L. A. J., & Lambin, P. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00018-1 · Cited by 24 · see citing works
Zong, H., Yang, J.-X., Zhang, Z., Li, Z., & Zhang, X. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01487-w · Cited by 22 · see citing works
Lombardo, G., Couvert, C., Kose, M., Begum, A., Spiertz, C., Worrell, C., Hasselbaink, D. M., Didden, E., Sforzini, L., Todorovic, M., et al. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104553 · Cited by 19 · see citing works
Patients’ selection and trial matching in early-phase oncology clinical trials
Corbaux, P., Bayle, A., Besle, S., Vinceneux, A., Vanacker, H., Ouali, K., Hanvic, B., Baldini, C., Cassier, P. A., Terret, C., et al. (2024) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2024.104307 · Cited by 18 · see citing works
The My Cancer Genome clinical trial data model and trial curation workflow
Jain, N. M., Mittendorf, K. F., Holt, M. E., Lenoue-Newton, M. L., Maurer, I., Miller, C. J., Stachowiak, M. R., Botyrius, M., Cole, J., Micheel, C., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa066 · Cited by 17 · see citing works
Kaskovich, S., Wyatt, K. D., Oliwa, T., Graglia, L., Furner, B., Lee, J., Mayampurath, A. M., & Volchenboum, S. L. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00009 · Cited by 17 · see citing works
The Data Gap in the EHR for Clinical Research Eligibility Screening.
Butler, A. M., Wei, W., Yuan, C., Kang, T., Si, Y., & Weng, C. (2018) · Europe PMC (PubMed Central) · Cited by 16 · see citing works
Cohort Selection for Clinical Trials From Longitudinal Patient Records: Text Mining Approach
Spasić, ., Krzemiński, D., Corcoran, P., & Balinsky, A. A. (2019) · JMIR Medical Informatics · doi:10.2196/15980 · Cited by 16 · see citing works
Mudaranthakam, D. P., Thompson, J. A., Hu, J., Pei, D., Chintala, S., Park, M., Fridley, B. L., Gajewski, B. J., Koestler, D. C., & Mayo, M. S. (2018) · JAMIA Open · doi:10.1093/jamiaopen/ooy023 · Cited by 15 · see citing works
A prospective pragmatic evaluation of automatic trial matching tools in a molecular tumor board
Gueguen, L., Olgiati, L., Brutti-Mairesse, C., Sans, A., Texier, V. L., & Verlingue, L. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00806-y · Cited by 14 · see citing works
Chen, H., Li, X., He, X., Chen, A., McGill, J. M., Webber, E. C., Xu, H., Liu, M., & Bian, J. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00071 · Cited by 14 · see citing works
Use of AI as a Research Recruitment Tool: Ethical Elements, Considerations, and Recommendations
Rentzepis, P. J., Snyder, D. L., Baker, T. J., & Bierer, B. E. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2649234 · Cited by 14 · see citing works
Lee, K., Mai, Y., Liu, Z., Raja, K., Higashi, M. K., Jun, T., Ma, M., Wang, T., Ai, L., Calay, E., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-77447-x · Cited by 13 · see citing works
Hybrid natural language processing tool for semantic annotation of medical texts in Spanish
Campillos-Llanos, L., Valverde-Mateos, A., & Capllonch-Carrión, A. (2025) · BMC Bioinformatics · doi:10.1186/s12859-024-05949-6 · Cited by 11 · see citing works
Evaluation of patient-level retrieval from electronic health record data for a cohort discovery task
Chamberlin, S. R., Bedrick, S. D., Cohen, A. M., Wang, Y., Wen, A., Liu, S., Liu, H., & Hersh, W. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa026 · Cited by 13 · see citing works
Musculoskeletal care – at the confluence of data science, sensors, engineering, and computation
Kataria, S., & Ravindran, V. K. (2022) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-022-05126-x · Cited by 12 · see citing works
Kersloot, M. G., Lau, F., Abu–Hanna, A., Arts, D. L., & Ronald Cornet, A. (2019) · Journal of Biomedical Semantics · doi:10.1186/s13326-019-0207-3 · Cited by 12 · see citing works
Smart Technology in Lung Disease Clinical Trials
Geller, N. L., Kim, D., & Tian, X. (2015) · CHEST Journal · doi:10.1378/chest.15-1314 · Cited by 12 · see citing works
A Time-and-Motion Study of Clinical Trial Eligibility Screening in a Pediatric Emergency Department
Dexheimer, J. W., Tang, H., Kachelmeyer, A. M., Hounchell, M., Kennebeck, S., Solti, I., & Ni, Y. (2018) · Pediatric Emergency Care · doi:10.1097/pec.0000000000001592 · Cited by 10 · see citing works
Self-citationWu, J., Yakubov, A., Abdul‐Hay, M., Love, E., Kroening, G., Cohen, D., Spalink, C., Joshi, A., Balar, A. V., Joseph, K.-A., et al. (2021) · JCO Oncology Practice · doi:10.1200/op.21.00629 · Cited by 10 · see citing works
Jasmir, J., Nurmaini, S., Malik, R. F., & Abidin, D. Z. (2020) · Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN) · doi:10.2991/aisr.k.200424.061 · Cited by 10 · see citing works
Artificial Intelligence in Cancer Diagnosis: A Game-changer in Healthcare
Sahoo, P., Kundu, M., & Begum, J. (2024) · Current Pharmaceutical Biotechnology · doi:10.2174/0113892010298852240528123911 · Cited by 8 · see citing works
Increasing Clinical Trial Accrual via Automated Matching of Biomarker Criteria
Chen, J. W., Kunder, C. A., Bui, N. Q., Zehnder, J. L., Costa, H. A., & Stehr, H. (2019) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811215636_0004 · Cited by 8 · see citing works
Use of Artificial Intelligence/Machine Learning in Cancer Research During the COVID-19 Pandemic
Kamal, V. K., & Kumari, D. (2020) · Asian Pacific Journal of Cancer Care · doi:10.31557/apjcc.2020.5.s1.251-253 · Cited by 8 · see citing works
Gui, X., Lv, H., Wang, X., Lv, L., Xiao, Y., & Wang, L. (2025) · BioData Mining · doi:10.1186/s13040-025-00458-5 · Cited by 7 · see citing works
“While traditional manual screening and automated approaches have attempted to address these challenges, they fall short in key areas [4].”
Patient2Trial: From patient to participant in clinical trials using large language models
Datta, S., Lee, K., Huang, L., Paek, H., Gildersleeve, R., Gold, J., Pillai, D., Wang, J., Higashi, M. K., Shi, L., et al. (2025) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2025.101615 · Cited by 7 · see citing works
Vaterkowski, M., Daniel, C., Rosa, A. L., Kalra, D., Jaulent, M., Lamé, G., & Kempf, E. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105900 · Cited by 7 · see citing works
Gross motor function prediction using natural language processing in cerebral palsy
Greve, K. R., Ni, Y., Bailes, A. F., Vargus‐Adams, J. N., Miley, A. E., Aronow, B. J., McMahon, M. M., Kurowski, B. G., & Mitelpunkt, A. (2022) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.15301 · Cited by 7 · see citing works
Self-citationRafee, A., Riepenhausen, S., Neuhaus, P., Meidt, A., Dugas, M., & Varghese, J. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01611-y · Cited by 7 · see citing works
The future of clinical trials in urological oncology
Narayan, V. M., & Dahm, P. (2019) · Nature Reviews Urology · doi:10.1038/s41585-019-0243-x · Cited by 6 · see citing works
Clinical Research Integration Within the Electronic Health Record
Johnson, E. A., & Carrington, J. M. (2020) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000000659 · Cited by 6 · see citing works
Opportunities for Patient Matching Algorithms to Improve Patient Care in Oncology
Johnson, T. S., Liebner, D. A., & Chen, J. L. (2017) · JCO Clinical Cancer Informatics · doi:10.1200/cci.16.00042 · Cited by 6 · see citing works
Gong, G., Liu, J., Pandya, S., Taborda, C., Wiesendanger, N., Price, N., Byron, W., Coppi, A. C., Young, P., Wiess, C., et al. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00262 · Cited by 5 · see citing works
Yang, Y., & Liu, X. (2026) · Exploration of Targeted Anti-tumor Therapy · doi:10.37349/etat.2026.1002357 · Cited by 5 · see citing works
Clinical trial screening in gynecologic oncology: Defining the need and identifying best practices
Castellano, T., Lara, O. D., McCormick, C. B., Chase, D., Bae-Jump, V. L., Jackson, A. L., Pepin, J. T., Ghamande, S., Moore, K. N., Pothuri, B., et al. (2024) · Gynecologic Oncology · doi:10.1016/j.ygyno.2024.11.009 · Cited by 5 · see citing works
Mallahan, S., Gajra, A., Blau, S. H., Peguero, J. A., Shoukier, M., Kanaan, M. N., Huynh, C. T., Efiom-Ekaha, D. N., Vankayala, H. M., Kitchen, K., et al. (2024) · AI in Precision Oncology · doi:10.1089/aipo.2024.0030 · Cited by 5 · see citing works
Mehari, M., Warrier, G., Dada, A., Kabir, A., Haskell-Mendoza, A. P., Tripathy, A., Jha, R., Nieblas‐Bedolla, E., Jackson, J. D., Gonzalez, A. T., et al. (2025) · Science Advances · doi:10.1126/sciadv.adt5708 · Cited by 5 · see citing works
Emerging Trends in Artificial Intelligence in Neuro-Oncology
Chadha, S., Sritharan, D. V., Hager, T., D’Souza, R., & Aneja, S. (2025) · Current Oncology Reports · doi:10.1007/s11912-025-01688-w · Cited by 5 · see citing works
Foucher, J., Azizi, L., Öijerstedt, L., Kläppe, U., & Ingre, C. (2024) · Systematic Reviews · doi:10.1186/s13643-024-02533-0 · Cited by 5 · see citing works
Clustered Automated Machine Learning (CAML) model for clinical coding multi-label classification
Mustafa, A., & Azghadi, M. R. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02349-3 · Cited by 5 · see citing works
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association StudiesPreprint
Stark, S. G., Hyland, S. L., Pradier, M. F., Lehmann, K.-V., WICKI, A. N., Pérez‐Cruz, F., Vogt, J. E., & Rätsch, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.12973 · Cited by 5 · see citing works
Rosa, A. L., Vaterkowski, M., Marc Cuggia, A., Campillo‐Gimenez, B., Tournigand, C., BAUJAT, B., Daniel, C., Kempf, E., & Lamé, G. (2024) · Cancer Medicine · doi:10.1002/cam4.70466 · Cited by 4 · see citing works
Development of a conceptual framework for defining trial efficiency
Xie, C. X., Simoni, A. D., Eldridge, S. M., Pinnock, H., & Relton, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304187 · Cited by 4 · see citing works
Booker, J., Penn, J., Noor, K., Dobson, R., Fersht, N. L., Funnell, J. P., Hill, C. S., Khan, D. Z., Newall, N., Searle, T., et al. (2025) · World Neurosurgery · doi:10.1016/j.wneu.2025.123907 · Cited by 4 · see citing works
OncoCTMiner: streamlining precision oncology trial matching via molecular profile analysis
Xu, Q., Liu, Y., Sun, D., Huang, X., Li, F., Zhai, J., Yang, L., Zhou, Q., Qian, N., & Niu, B. (2023) · Database · doi:10.1093/database/baad077 · Cited by 4 · see citing works
Naceanceno, K. S., House, S. L., & Asaro, P. V. (2021) · Applied Clinical Informatics · doi:10.1055/s-0041-1727153 · Cited by 4 · see citing works
Natural Language Processing: Applications in Pediatric Research
Savova, G., Pestian, J. P., Connolly, B., Miller, T. A., Ni, Y., & Dexheimer, J. W. (2016) · Translational bioinformatics · doi:10.1007/978-981-10-1104-7_12 · Cited by 4 · see citing works
Self-citationClosed-World Semantics for Conjunctive Queries with Negation over ELH⊥ Ontologies
Borgwardt, S., & Forkel, W. (2019) · doi:10.25368/2023.222 · Cited by 4 · see citing works
Clinical Research Informatics: Supporting the Research Study Lifecycle
Johnson, S. B. (2017) · Yearbook of Medical Informatics · doi:10.1055/s-0037-1606503 · Cited by 3 · see citing works
Chang, E., & Mostafa, J. (2022) · PubMed Central · Cited by 3 · see citing works
Ronquillo, J. G., & Lester, W. T. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.21.00018 · Cited by 3 · see citing works
Llanos, L. C., Valverde-Mateos, A., Capllonch-Carrión, A., González-Quevedo, D., López-Urbán, M. R., Hernando-Tundidor, M. S., Puig, S. Z., & Heras, J. J. (2026) · Scientific Data · doi:10.1038/s41597-026-06608-6 · Cited by 3 · see citing works
Berre, C. L., Jairath, V., Panaccione, R., Bourreille, A., Magro, F., Danese, S., & Peyrin‐Biroulet, L. (2025) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2025.01.030 · Cited by 2 · see citing works
AI in Clinical Trial Design and Patient Recruitment
Yingngam, B. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6270-9.ch002 · Cited by 2 · see citing works
Claessens, A., Simon, A., Manchart, A., Desmonts, L., Leguay, C., Faivre, J.-C., Cambier, S., Salleron, J., & Lambert, A. (2026) · JMIR Cancer · doi:10.2196/80268 · Cited by 2 · see citing works
Gedor, M., Désandes, E., Chesnel, M., Merlin, J., Marchal, F., Lambert, A., & Baudin, A. (2024) · Bulletin du Cancer · doi:10.1016/j.bulcan.2024.01.010 · Cited by 2 · see citing works
Applying Artificial Intelligence in Pediatric Clinical Trials: Potential Impacts and Obstacles
Foote, H. P., Cohen‐Wolkowiez, M., Lindsell, C. J., & Hornik, C. P. V. (2024) · The Journal of Pediatric Pharmacology and Therapeutics · doi:10.5863/1551-6776-29.3.336 · Cited by 2 · see citing works
- Authors
- Ni, Y., Wright, J., Perentesis, J., Lingren, T., Deleger, L., Kaiser, M., Kohane, I., & Solti, I.
- Affiliations
- Cincinnati Children's Hospital Medical Center (US)
- Harvard University (US)
- Corresponding author
- Ni, Yizhao
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2015-04-14
- Volume
- 15
- Issue
- 1
- Pages
- 28-28
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC4407835
- OpenAlex
- W2116972869
- Cited by
- 142
- References
- 35
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 20 · 2023: 11 · 2022: 12 · 2021: 21 · 2020: 17 · 2019: 13 · 2018: 8 · 2017: 5 · 2016: 5 · 2015: 1
- FWCI
- 12.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- Cincinnati Children's Hospital Medical Center
- National Institutes of Health (UL1TR001425, UL1TR000077, 1r21hd072883-01)
- National Human Genome Research Institute (U01HG006828)
- U.S. National Library of Medicine (R00LM010227)
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21 HD072883, 1R21HD072883-01)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- clinical trial eligibility screening, pediatric oncology, natural language processing, information extraction, electronic health records, clinical trial recruitment, physician workload reduction
- MeSH terms
- Child, Clinical Trials as Topic, Eligibility Determination, Humans, Natural Language Processing, Neoplasms, Information Storage and Retrieval, Patient Selection
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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