@inproceedings{dc3b2f2568b9479485c04eeb6a26348a,
title = "Trend and network analysis of common eligibility features for cancer trials in ClinicalTrials.gov",
abstract = "ClinicalTrials.gov has been archiving clinical trials since 1999, with > 165,000 trials at present. It is a valuable but relatively untapped resource for understanding trial design patterns and acquiring reusable trial design knowledge. We extracted common eligibility features using an unsupervised tag-mining method and mined their temporal usage patterns in clinical trials on various cancers. We then employed trend and network analysis to investigate two questions: (1) what eligibility features are frequently used to select patients for clinical trials within one cancer or across multiple cancers; and (2) what are the trends in eligibility feature adoption or discontinuation across cancer research domains? Our results showed that each cancer domain reuses a small set of eligibility features frequently for selecting cancer trial patients and some features are shared across different cancers, with value range adjustments for numerical measures. We discuss the implications for facilitating community-based clinical research knowledge sharing and reuse. {\textcopyright} 2014 Springer International Publishing.",
author = "C. Weng and A. Yaman and K. Lin and Z. He",
year = "2014",
doi = "10.1007/978-3-319-08416-9_13",
language = "English",
isbn = "9783319084152",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "130--141",
booktitle = "Smart Health - International Conference, ICSH 2014, Proceedings",
note = "2nd International Conference for Smart Health, CSH 2014 ; Conference date: 10-07-2014 Through 11-07-2014",
}