Assessing the population representativeness of colorectal cancer treatment clinical trials

He, Zhe; Chen, Zhiwei; George, Thomas J.; Lipori, Gloria; Bian, Jiang

doi:10.1109/embc.2016.7591353

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Rubin et al [ 14 ] categorized the eligibility criteria of three cancers into 24 categories and developed the tool for authoring new clinical trial protocols based on similarity among eligibility criteria. He et al [ 15 ] collected eligibility criteria from colorectal cancer treatment clinical trials and assessed the population representativeness in quantitative and qualitative aspects respectively. Van Spall et al [ 16 ] selected eligibility criteria from the randomized controlled trials published in high impact journal, and characterized the nature, extend and contribution of 38 categories of exclusion criteria.…”

Section: Introductionmentioning

confidence: 99%

Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods

Zong

Yang

Zhang

et al. 2021

BMC Med Inform Decis Mak

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Background Semantic categorization analysis of clinical trials eligibility criteria based on natural language processing technology is crucial for the task of optimizing clinical trials design and building automated patient recruitment system. However, most of related researches focused on English eligibility criteria, and to the best of our knowledge, there are no researches studied the Chinese eligibility criteria. Thus in this study, we aimed to explore the semantic categories of Chinese eligibility criteria. Methods We downloaded the clinical trials registration files from the website of Chinese Clinical Trial Registry (ChiCTR) and extracted both the Chinese eligibility criteria and corresponding English eligibility criteria. We represented the criteria sentences based on the Unified Medical Language System semantic types and conducted the hierarchical clustering algorithm for the induction of semantic categories. Furthermore, in order to explore the classification performance of Chinese eligibility criteria with our developed semantic categories, we implemented multiple classification algorithms, include four baseline machine learning algorithms (LR, NB, kNN, SVM), three deep learning algorithms (CNN, RNN, FastText) and two pre-trained language models (BERT, ERNIE). Results We totally developed 44 types of semantic categories, summarized 8 topic groups, and investigated the average incidence and prevalence in 272 hepatocellular carcinoma related Chinese clinical trials. Compared with the previous proposed categories in English eligibility criteria, 13 novel categories are identified in Chinese eligibility criteria. The classification result shows that most of semantic categories performed quite well, the pre-trained language model ERNIE achieved best performance with macro-average F1 score of 0.7980 and micro-average F1 score of 0.8484. Conclusion As a pilot study of Chinese eligibility criteria analysis, we developed the 44 semantic categories by hierarchical clustering algorithms for the first times, and validated the classification capacity with multiple classification algorithms.

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Section: Introductionmentioning

confidence: 99%

Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods

Zong

Yang

Zhang

et al. 2021

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

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“…Only 794 (4.4%) of all the 17,858 interventional clinical studies in ClinicalTrials.gov on lung neoplasms between 2000 and 2014 explicitly excluded patients >= 65 years old. Our recent study also shows that the quantitative eligibility criteria in cancer studies did not pose notable restrictions in the target population [10]. However, the qualitative eligibility criteria with moderate or strict restrictions were frequently observed in these trials.…”

Section: Introductionmentioning

confidence: 91%

Analysis of temporal constraints in qualitative eligibility criteria of cancer clinical studies

Chen

Bian

2016

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Clinical studies, especially randomized controlled trials, generate gold-standard medical evidence. However, the lack of population representativeness of clinical studies has hampered their generalizability to the real-world population. Overly restrictive qualitative criteria are often applied to exclude patients. In this work, we develop a lexical-pattern-based tool to structure qualitative eligibility criteria with temporal constraints, with which we analyzed over 10,800 cancer clinical studies. Our results showed that restrictive temporal constraints are often applied on qualitative criteria in cancer studies, limiting the generalizability of their results.

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A 10-year review of survival among patients with metastatic gastrointestinal cancers: a population-based study

Abdel‐Rahman

2020

Int J Colorectal Dis

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Assessing the population representativeness of colorectal cancer treatment clinical trials

Cited by 3 publications

References 11 publications

Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods

Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods

Analysis of temporal constraints in qualitative eligibility criteria of cancer clinical studies

A 10-year review of survival among patients with metastatic gastrointestinal cancers: a population-based study

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