{"id":376130,"date":"2026-03-09T16:39:37","date_gmt":"2026-03-09T16:39:37","guid":{"rendered":"https:\/\/www.europesays.com\/ie\/376130\/"},"modified":"2026-03-09T16:39:37","modified_gmt":"2026-03-09T16:39:37","slug":"a-large-scale-database-for-clinical-trial-outcomes-and-features","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ie\/376130\/","title":{"rendered":"A large-scale database for clinical trial outcomes and features"},"content":{"rendered":"
  • \n

    Global R&D expenditure for pharmaceuticals. Statista https:\/\/www.statista.com\/statistics\/309466\/global-r-and-d-expenditure-for-pharmaceuticals\/#::\u0303text=In%202022%2C%20research%20and%20development,principal%20agency%20associated%20with%20processes<\/a> (2025).<\/p>\n<\/li>\n

  • \n

    Grand View Research Clinical trials market size, share & trends analysis report by phase (phase I, phase II, phase III, phase IV), by study design, by indication (pain management, oncology, CNS condition, diabetes, obesity), by region, and segment forecasts, 2022\u20132030. Research and Markets https:\/\/www.researchandmarkets.com\/reports\/4396385\/clinical-trials-market-size-share-and-trends<\/a> (2022).<\/p>\n<\/li>\n

  • \n

    Scannell, J. W., Blanckley, A., Boldon, H. & Warrington, B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 11<\/b>, 191\u2013200 (2012).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M. & Granger, C. B. Fundamentals of Clinical Trials (Springer, 2015).<\/p>\n<\/li>\n

  • \n

    Sinha, K., Ghosh, N. & Sil, P. C. A review on the recent applications of deep learning in predictive drug toxicological studies. Chem. Res. Toxicol. 36<\/b>, 1174\u20131205 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Lu, Y. et al. Machine learning for synthetic data generation: a review. Preprint at https:\/\/arxiv.org\/abs\/2302.04062<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Ross, J. S., Mulvey, G. K., Hines, E. M., Nissen, S. E. & Krumholz, H. M. Trial publication after registration in ClinicalTrials.gov: a cross-sectional analysis. PLoS Med. 6<\/b>, e1000144 (2009).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zarin, D. A., Tse, T., Williams, R. J., Califf, R. M. & Ide, N. C. The ClinicalTrials.gov results database\u2014update and key issues. N. Engl. J. Med. 364<\/b>, 852\u2013860 (2011).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zarin, D. A., Tse, T., Williams, R. J. & Carr, S. Trial reporting in ClinicalTrials.gov\u2014the final rule. N. Engl. J. Med. 375<\/b>, 1998\u20132004 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Johnson, A. E. W. et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10<\/b>, 1 (2023).<\/p>\n<\/li>\n

  • \n

    Chen, J. et al. Trialbench: multi-modal artificial intelligence-ready clinical trial datasets. Preprint at https:\/\/arxiv.org\/abs\/2407.00631<\/a> (2024).<\/p>\n<\/li>\n

  • \n

    Rajpurkar, P. et al. Evaluation of a machine learning model based on pretreatment symptoms and electroencephalographic features to predict outcomes of antidepressant treatment in adults with depression: a prespecified secondary analysis of a randomized clinical trial. JAMA Netw. Open 3<\/b>, e206653 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hong, Z.-Y., Shim, J., Son, W. C. & Hwang, C. Predicting successes and failures of clinical trials with an ensemble LS-SVR. Preprint at medRxiv https:\/\/doi.org\/10.1101\/2020.02.05.20020636<\/a> (2020).<\/p>\n<\/li>\n

  • \n

    Qi, Y. & Tang, Q. Predicting phase 3 clinical trial results by modeling phase 2 clinical trial subject level data using deep learning. In Proc. 4th Machine Learning for Healthcare Conference (eds Doshi-Velez, F. et al.) 288\u2013303 (PMLR, 2019).<\/p>\n<\/li>\n

  • \n

    Lo, A. W., Siah, K. W. & Wong, C. H. Machine learning with statistical imputation for predicting drug approvals. Harvard Data Science Review 1<\/b>, 1 (2019).<\/p>\n<\/li>\n

  • \n

    Stergiopoulos, S., Getz, K. A. & Blazynski, C. Evaluating the completeness of ClinicalTrials.gov. Ther. Innov. Regul. Sci. 53<\/b>, 307\u2013317 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Feijoo, F., Palopoli, M., Bernstein, J., Siddiqui, S. & Albright, T. E. Key indicators of phase transition for clinical trials through machine learning. Drug Discov. Today 25<\/b>, 414\u2013421 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Gayvert, K. M., Madhukar, N. S. & Elemento, O. A data-driven approach to predicting successes and failures of clinical trials. Cell Chem. Biol. 23<\/b>, 1294\u20131301 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Artemov, A. V. et al. Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes. Preprint at bioRxiv https:\/\/doi.org\/10.1101\/095653<\/a> (2016).<\/p>\n<\/li>\n

  • \n

    Willigers, B. J., Nagarajan, S., Ghiorghui, S., Darken, P. & Lennard, S. Algorithmic benchmark modulation: a novel method to develop success rates for clinical studies. Clin. Trials https:\/\/doi.org\/10.1177\/17407745231207858<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Fu, T., Huang, K. & Sun, J. Automated prediction of clinical trial outcome. US patent 17\/749,065 (2023).<\/p>\n<\/li>\n

  • \n

    Malik, L. et al. Predicting success in regulatory approval from phase I results. Cancer Chemother. Pharmacol. 74<\/b>, 1099\u20131103 (2014).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    DiMasi, J. et al. A tool for predicting regulatory approval after phase II testing of new oncology compounds. Clin. Pharmacol. Ther. 98<\/b>, 506\u2013513 (2015).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Improving public access to aggregate content of ClinicalTrials.gov. CTTI https:\/\/aact.ctti-clinicaltrials.org\/<\/a> (2024).<\/p>\n<\/li>\n

  • \n

    Fu, T., Huang, K., Xiao, C., Glass, L. M. & Sun, J. HINT: hierarchical interaction network for clinical-trial-outcome predictions. Patterns 3<\/b>, 100445 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    McHugh, M. L. Interrater reliability: the kappa statistic. Biochem. Med. 22<\/b>, 276\u2013282 (2012).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wang, Z., Xiao, C. & Sun, J. SPOT: sequential predictive modeling of clinical trial outcome with meta-learning. In Proc. 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics 1\u201311 (ACM, 2023).<\/p>\n<\/li>\n

  • \n

    Lu, Y. et al. Uncertainty quantification and interpretability for clinical trial approval prediction. Health Data Sci. 4<\/b>, 0126 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Aliper, A. et al. Prediction of clinical trials outcomes based on target choice and clinical trial design with multi-modal artificial intelligence. Clin. Pharmacol. Ther. 114<\/b>, 972\u2013980 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Califf, R. M. et al. Characteristics of clinical trials registered in ClinicalTrials.gov, 2007\u20132010. JAMA 307<\/b>, 1838\u20131847 (2012).<\/p>\n<\/li>\n

  • \n

    Tasneem, A. et al. The database for aggregate analysis of ClinicalTrials.gov (AACT) and subsequent regrouping by clinical specialty. PLoS ONE 7<\/b>, e33677 (2012).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Anderson, M. L. et al. Compliance with results reporting at ClinicalTrials.gov. N. Engl. J. Med. 372<\/b>, 1031\u20131039 (2015).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Huser, V. & Cimino, J. J. Linking ClinicalTrials.gov and PubMed to track results of interventional human clinical trials. PLoS ONE 8<\/b>, e68409 (2013).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D. & R\u00e9, C. Data programming: creating large training sets, quickly. Adv. Neural Inf. Process. Syst. 29<\/b>, 3567\u20133575 (2016).<\/p>\n

    PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n","protected":false},"excerpt":{"rendered":"Global R&D expenditure for pharmaceuticals. Statista https:\/\/www.statista.com\/statistics\/309466\/global-r-and-d-expenditure-for-pharmaceuticals\/#::\u0303text=In%202022%2C%20research%20and%20development,principal%20agency%20associated%20with%20processes (2025). Grand View Research Clinical trials market size, share & trends…\n","protected":false},"author":2,"featured_media":376131,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[275],"tags":[243,3541,18,910,135,475,146636,2100,146635,474,19,17,9470,7482],"class_list":{"0":"post-376130","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-healthcare","8":"tag-drug-development","9":"tag-drug-discovery","10":"tag-eire","11":"tag-general","12":"tag-health","13":"tag-health-care","14":"tag-health-economics","15":"tag-health-promotion-and-disease-prevention","16":"tag-health-psychology","17":"tag-healthcare","18":"tag-ie","19":"tag-ireland","20":"tag-maternal-and-child-health","21":"tag-medicine-public-health"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@ie\/116200208013419420","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/376130","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/comments?post=376130"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/376130\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media\/376131"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media?parent=376130"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/categories?post=376130"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/tags?post=376130"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}