Optimizing clinical trials recruitment via deep learning
- PMID: 31188432
- PMCID: PMC7647233
- DOI: 10.1093/jamia/ocz064
Optimizing clinical trials recruitment via deep learning
Abstract
Objective: Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive process where investigators aim to enroll volunteers with predetermined characteristics, administer treatment(s), and collect safety and efficacy data. Therefore, choosing top-enrolling investigators is essential for efficient clinical trial execution and is 1 of the primary drivers of drug development cost.
Materials and methods: To facilitate clinical trials optimization, we propose DeepMatch (DM), a novel approach that builds on top of advances in deep learning. DM is designed to learn from both investigator and trial-related heterogeneous data sources and rank investigators based on their expected enrollment performance on new clinical trials.
Results: Large-scale evaluation conducted on 2618 studies provides evidence that the proposed ranking-based framework improves the current state-of-the-art by up to 19% on ranking investigators and up to 10% on detecting top/bottom performers when recruiting investigators for new clinical trials.
Discussion: The extensive experimental section suggests that DM can provide substantial improvement over current industry standards in several regards: (1) the enrollment potential of the investigator list, (2) the time it takes to generate the list, and (3) data-informed decisions about new investigators.
Conclusion: Due to the great significance of the problem at hand, related research efforts are set to shift the paradigm of how investigators are chosen for clinical trials, thereby optimizing and automating them and reducing the cost of new therapies.
Keywords: clinical trials; deep learning; deep matching; electronic health records; pointwise ranking.
© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Figures
Similar articles
-
Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217. Cochrane Database Syst Rev. 2022. PMID: 36321557 Free PMC article.
-
The future of Cochrane Neonatal.Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12. Early Hum Dev. 2020. PMID: 33036834
-
Cohort selection for clinical trials using deep learning models.J Am Med Inform Assoc. 2019 Nov 1;26(11):1181-1188. doi: 10.1093/jamia/ocz139. J Am Med Inform Assoc. 2019. PMID: 31532478 Free PMC article.
-
Recruiting minorities into clinical trials: toward a participant-friendly system.J Natl Cancer Inst. 1995 Dec 6;87(23):1747-59. doi: 10.1093/jnci/87.23.1747. J Natl Cancer Inst. 1995. PMID: 7473831 Review.
-
Management of clinical trials with new medications for cocaine dependence and abuse.NIDA Res Monogr. 1997;175:96-117. NIDA Res Monogr. 1997. PMID: 9467794 Review.
Cited by
-
Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.J Am Med Inform Assoc. 2024 Nov 1;31(11):2749-2759. doi: 10.1093/jamia/ocae243. J Am Med Inform Assoc. 2024. PMID: 39259922 Free PMC article. Review.
-
FRAMM: Fair ranking with missing modalities for clinical trial site selection.Patterns (N Y). 2024 Mar 1;5(3):100944. doi: 10.1016/j.patter.2024.100944. eCollection 2024 Mar 8. Patterns (N Y). 2024. PMID: 38487797 Free PMC article.
-
Enhancing site selection strategies in clinical trial recruitment using real-world data modeling.PLoS One. 2024 Mar 11;19(3):e0300109. doi: 10.1371/journal.pone.0300109. eCollection 2024. PLoS One. 2024. PMID: 38466688 Free PMC article.
-
Artificial Intelligence Applied to clinical trials: opportunities and challenges.Health Technol (Berl). 2023;13(2):203-213. doi: 10.1007/s12553-023-00738-2. Epub 2023 Feb 28. Health Technol (Berl). 2023. PMID: 36923325 Free PMC article. Review.
-
ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials.BMC Med Res Methodol. 2022 May 14;22(1):141. doi: 10.1186/s12874-022-01611-y. BMC Med Res Methodol. 2022. PMID: 35568796 Free PMC article.
References
-
- Friedman, Lawrence M., Curt Furberg, David L. DeMets, David M. Reboussin, and Christopher B. Granger. Fundamentals of clinical trials. Vol. 4. New York: Springer, 2010.
-
- Martin L, Hutchens M, Hawkins C, et al. How much do clinical trials cost? Nat Rev Drug Discov 2017; 166: 381–82. - PubMed
-
- Mullard A. 2016 FDA drug approvals. Nat Rev Drug Discov 2017; 162: 73–6. - PubMed
-
- How Do CROs Choose Sites and Investigators for Their Clinical Trials? https://www.theclinicaltrialsguru.com/blog1/how-do-cros-choose-sites-and.... Accessed January 2019.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical