Clinic Match: Facilitating Clinical Trials with Text Mining
Abstract
Clinical trials are fundamental to advancing scientific knowledge, making it possible to assess the safety and efficacy of new treatments in humans in an ethical, controlled and systematic manner. As well as contributing to the advancement of medicine, trials provide access to potentially promising therapies that are not commercially available, which is particularly important for individuals with serious diseases or conditions without adequate treatment options. However, recruiting participants for clinical trials can face a number of challenges, the most common of which is finding and recruiting a sufficiently large number of participants who meet the trial’s eligibility criteria. Generally, recruitment relies on time-consuming manual reviews of medical records, facing high screening failure rates. In addition, many cases, particularly in oncology, depend on the correct timing for entry into a clinical trial. To overcome these challenges, this ongoing study proposes developing a tool to help doctors find open trials that are compatible with the specific case they are treating. This approach seeks to quickly analyze clinical trials that are potentially compatible with the case, allowing us to test the hypothesis of the impact on the recruitment of volunteers for trials in the future.
References
Ashery, R. S. and McAuliffe, W. E. (1992). Implementation issues and techniques in randomized trials of outpatient psychosocial treatments for drug abusers: Recruitment of subjects. The American Journal of Drug and Alcohol Abuse, 18(3):305––329.
Bossuyt, P. M., Reitsma, J. B., Linnet, K., and Moons, K. G. (2012). Beyond Diagnostic Accuracy: The Clinical Utility of Diagnostic Tests. Clinical Chemistry, 58(12):1636–1643.
Chondrogiannis, E., Andronikou, V., Tagaris, A., Karanastasis, E., Varvarigou, T., and Tsuji, M. (2017). A novel semantic representation for eligibility criteria in clinical trials. Journal of Biomedical Informatics, 69:10—-23.
Ellis, P. (2000). Attitudes towards and participation in randomised clinical trials in oncology: A review of the literature. Annals of Oncology, 11(8):939––946.
Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D. G., Hearst, N., and Newman, T. B. (2000). Designing clinical research. Lippincott Williams and Wilkins, Philadelphia, PA, 2 edition.
Jonnalagadda, S. R., Adupa, A. K., Garg, R. P., Corona-Cox, J., and Shah, S. J. (2017). Text mining of the electronic health record: An information extraction approach for automated identification and subphenotyping of hfpef patients for clinical trials. Journal of Cardiovascular Translational Research, 10(3):313—-321.
Patel, M. X., Doku, V., and Tennakoon, L. (2003). Challenges in recruitment of research participants. Advances in Psychiatric Treatment, 9(3):229—-238.
Pressler, T. R., Yen, P.-Y., Ding, J., Liu, J., Embi, P. J., and Payne, P. R. O. (2012). Computational challenges and human factors influencing the design and use of clinical research participant eligibility pre-screening tools. BMC Medical Informatics and Decision Making, 12(1).
Sen, A., Ryan, P. B., Goldstein, A., Chakrabarti, S., Wang, S., Koski, E., and Weng, C. (2016). Correlating eligibility criteria generalizability and adverse events using big data for patients and clinical trials. Annals of the New York Academy of Sciences, 1387(1):34––43.
