A knowledge base of clinical trial eligibility criteria

Líu, Hao; Yuan, Chi; Butler, Alex; Sun, Yingcheng; Weng, Chunhua

doi:10.1016/j.jbi.2021.103771

Cited by 22 publications

(9 citation statements)

References 23 publications

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“…Afterwards, the full text screening step analyzed in total 29 articles, out of which 15 were either focused on AI and cancer but without using the OMOP or focused solely on cancer or AI. The remaining nine articles either contained cancer studies on OMOP-based data not using predictive AI models [ 11 , 33 , 34 , 35 , 36 ] or performed predictive analysis on OMOP-based data of a non-cancerous disease [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. An example for the first group are preliminary studies that are focused on harmonizing data in the OMOP using extract, load, and transform (ETL) processes.…”

Section: Resultsmentioning

confidence: 99%

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review

Ahmadi

Peng

Wolfien

et al. 2022

IJMS

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The current generation of sequencing technologies has led to significant advances in identifying novel disease-associated mutations and generated large amounts of data in a high-throughput manner. Such data in conjunction with clinical routine data are proven to be highly useful in deriving population-level and patient-level predictions, especially in the field of cancer precision medicine. However, data harmonization across multiple national and international clinical sites is an essential step for the assessment of events and outcomes associated with patients, which is currently not adequately addressed. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an internationally established research data repository introduced by the Observational Health Data Science and Informatics (OHDSI) community to overcome this issue. To address the needs of cancer research, the genomic vocabulary extension was introduced in 2020 to support the standardization of subsequent data analysis. In this review, we evaluate the current potential of the OMOP CDM to be applicable in cancer prediction and how comprehensively the genomic vocabulary extension of the OMOP can serve current needs of AI-based predictions. For this, we systematically screened the literature for articles that use the OMOP CDM in predictive analyses in cancer and investigated the underlying predictive models/tools. Interestingly, we found 248 articles, of which most use the OMOP for harmonizing their data, but only 5 make use of predictive algorithms on OMOP-based data and fulfill our criteria. The studies present multicentric investigations, in which the OMOP played an essential role in discovering and optimizing machine learning (ML)-based models. Ultimately, the use of the OMOP CDM leads to standardized data-driven studies for multiple clinical sites and enables a more solid basis utilizing, e.g., ML models that can be reused and combined in early prediction, diagnosis, and improvement of personalized cancer care and biomarker discovery.

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Section: Resultsmentioning

confidence: 99%

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review

Ahmadi

Peng

Wolfien

et al. 2022

IJMS

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“…NER, assertion status detection, relation extraction, and entity linking features have been primarily used to extract relevant fields from clinical trial eligibility criteria. 112–116 These were mapped against relevant fields extracted from unstructured patient EHRs using the same techniques for efficient patient-cohort matching. 114 , 117–120 Recently, advanced models such as Criteria2Query 121 which uses an Information Extraction pipeline integrated with a Natural Language Interface, DeepEnroll 122 which uses hierarchical embeddings and COMPOSE 123 which uses word embeddings on clinical trials eligibility criteria along with a pseudo-Siamese network have provided significant improvement in the patient-trial matching process.…”

Section: Resultsmentioning

confidence: 99%

How can natural language processing help model informed drug development?: a review

et al. 2022

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ObjectiveTo summarize applications of natural language processing (NLP) in model informed drug development (MIDD) and identify potential areas of improvement.Materials and MethodsPublications found on PubMed and Google Scholar, websites and GitHub repositories for NLP libraries and models. Publications describing applications of NLP in MIDD were reviewed. The applications were stratified into 3 stages: drug discovery, clinical trials, and pharmacovigilance. Key NLP functionalities used for these applications were assessed. Programming libraries and open-source resources for the implementation of NLP functionalities in MIDD were identified.ResultsNLP has been utilized to aid various processes in drug development lifecycle such as gene-disease mapping, biomarker discovery, patient-trial matching, adverse drug events detection, etc. These applications commonly use NLP functionalities of named entity recognition, word embeddings, entity resolution, assertion status detection, relation extraction, and topic modeling. The current state-of-the-art for implementing these functionalities in MIDD applications are transformer models that utilize transfer learning for enhanced performance. Various libraries in python, R, and Java like huggingface, sparkNLP, and KoRpus as well as open-source platforms such as DisGeNet, DeepEnroll, and Transmol have enabled convenient implementation of NLP models to MIDD applications.DiscussionChallenges such as reproducibility, explainability, fairness, limited data, limited language-support, and security need to be overcome to ensure wider adoption of NLP in MIDD landscape. There are opportunities to improve the performance of existing models and expand the use of NLP in newer areas of MIDD.ConclusionsThis review provides an overview of the potential and pitfalls of current NLP approaches in MIDD.

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“…Future work could employ Natural Language Processing methods to extract information and construct features from the free-text data for enrollment rate prediction. Natural Language Processing methods applied to the free-text data from ClinicalTrials.gov has been explored for various applications including construction of knowledge base for eligibility criterion [ 34 ], illustration of relationships among multiple clinical trials [ 35 ], and literature mining for trends and prevalence of clinical instruments [ 36 ]. However, using Natural Language Processing methods for feature construction for predictive task using the ClinicalTrials.gov data is an untapped area.…”

Section: Discussionmentioning

confidence: 99%

Prediction of clinical trial enrollment rates

Bieganek

Aliferis

2022

PLoS ONE

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Clinical trials represent a critical milestone of translational and clinical sciences. However, poor recruitment to clinical trials has been a long standing problem affecting institutions all over the world. One way to reduce the cost incurred by insufficient enrollment is to minimize initiating trials that are most likely to fall short of their enrollment goal. Hence, the ability to predict which proposed trials will meet enrollment goals prior to the start of the trial is highly beneficial. In the current study, we leveraged a data set extracted from ClinicalTrials.gov that consists of 46,724 U.S. based clinical trials from 1990 to 2020. We constructed 4,636 candidate predictors based on data collected by ClinicalTrials.gov and external sources for enrollment rate prediction using various state-of-the-art machine learning methods. Taking advantage of a nested time series cross-validation design, our models resulted in good predictive performance that is generalizable to future data and stable over time. Moreover, information content analysis revealed the study design related features to be the most informative feature type regarding enrollment. Compared to the performance of models built with all features, the performance of models built with study design related features is only marginally worse (AUC = 0.78 ± 0.03 vs. AUC = 0.76 ± 0.02). The results presented can form the basis for data-driven decision support systems to assess whether proposed clinical trials would likely meet their enrollment goal.

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A knowledge base of clinical trial eligibility criteria

Cited by 22 publications

References 23 publications

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review

OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review

How can natural language processing help model informed drug development?: a review

Prediction of clinical trial enrollment rates

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