EMR2vec: Bridging the gap between patient data and clinical trial

Dhayne, Houssein; Kilany, Rima; Haque, Rafiqul; Taher, Yéhia

doi:10.1016/j.cie.2021.107236

Cited by 17 publications

(10 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to using EHR data for matching with clinical trials, studies have also been conducted on EMR data. Houssein [ 36 ] proposed the medical big data platform EMR2vec, which allows users to match, correlate and query EMR data and clinical trials. Like the NLP in the previous article, there are competitions that release questions related to patient matching every year.…”

Section: Resultsmentioning

confidence: 99%

A review of research on eligibility criteria for clinical trials

Cheng

Huang

2023

Clin Exp Med

View full text Add to dashboard Cite

The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials. Inappropriate eligibility criteria will lead to insufficient recruitment, which is an important reason for the eventual failure of many clinical trials. We have investigated the research status of eligibility criteria for clinical trials on academic platforms such as arXiv and NIH. We have classified and sorted out all the papers we found, so that readers can understand the frontier research in this field. Eligibility criteria are the most important part of a clinical trial study. The ultimate goal of research in this field is to formulate more scientific and reasonable eligibility criteria and speed up the clinical trial process. The global research on the eligibility criteria of clinical trials is mainly divided into four main aspects: natural language processing, patient pre-screening, standard evaluation, and clinical trial query. Compared with the past, people are now using new technologies to study eligibility criteria from a new perspective (big data). In the research process, complex disease concepts, how to choose a suitable dataset, how to prove the validity and scientific of the research results, are challenges faced by researchers (especially for computer-related researchers). Future research will focus on the selection and improvement of artificial intelligence algorithms related to clinical trials and related practical applications such as databases, knowledge graphs, and dictionaries.

show abstract

Section: Resultsmentioning

confidence: 99%

A review of research on eligibility criteria for clinical trials

Cheng

Huang

2023

Clin Exp Med

View full text Add to dashboard Cite

show abstract

“…Over the past few months, deep learning has shown good performance in the application of COVID-19 research. For instance, the multi-objective differential evolution algorithm has been applied to tune the initial parameters of convolution neural networks in order to identify the COVID-19 patients from chest CT images ( Singh, Kumar, & Kaur, 2020 ), and deep learning techniques have been introduced to link potential patients to suitable clinical trials ( Dhayne, Kilany, Haque, & Taher, 2021 ). Nevertheless, although many studies have focused on exploring the deep learning techniques for the COVID-19 infection detection, there is little research to measure the effect of social distancing on the spread of COVID-19.…”

Section: Literature Reviewmentioning

confidence: 99%

“… Estimate the effect of social distancing in terms of mobility metrics and then explore the proposed IPSO-DNN hybrid model to predict the effect of social distancing on the spread of COVID-19. Dhayne et al, 2021 Introduced deep learning techniques to link potential patients to suitable clinical trials. Te Vrugt et al, 2020 Developed an extended model for disease spread based on combining an SIR model with a dynamical density functional theory where social distancing is explicitly considered.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing deep neural networks to predict the effect of social distancing on COVID-19 spread

Liu¹,

Ding²,

Dong³

et al. 2022

Computers & Industrial Engineering

View full text Add to dashboard Cite

“…BERT has been applied in several clinical trial-related NLP tasks, including eligibility criteria classification [14], eligibility criteria and EMR similarity search [15], NER [16], [17], [18], [19] and relation extraction [17]. The NER and relation extraction tasks are most relevant to our work.…”

Section: More Recently Deep Learning-based Information Extraction Met...mentioning

confidence: 99%

Clinical Trial Information Extraction with BERT

Liu¹,

Hersch²,

Khalil³

et al. 2022

Preprint

View full text Add to dashboard Cite

Background Clinical trials are the gateway to new treatments. Clinical trial designs are documented in unstructured text and insights from natural language processing (NLP) of the documents can be useful in new trial design. Here we identify entity and relation types relevant to clinical trial design and propose a framework called CT-BERT to extract entities and relations from the clinical trial documents. Methods CT-BERT uses BERT models for named entity recognition (NER) and a hybrid approach for relation extraction including rule-based and machine learning-based models. We trained NER models to extract eligibility criteria entities by fine-tuning a set of pre-trained BERT models, including BERT, BioBERT, ClinicalBERT and BlueBERT. We then developed a rule-based relation extraction model to extract relations based on the NER results. For comparison, we also trained machine learning models using BERT to classify entity pairs into relation types. We compared the performance of CT-BERT with recent baseline methods including attention-based BiLSTM (Att-BiLSTM) and Criteria2Query. Results The NER training result shows that BioBERT large fine-tuned on clinical trials data achieves the best F1 score and is thus selected as the optimal NER model for CT-BERT. The result on a benchmark dataset showed that CT-BERT NER outperforms Att-BiLSTM and Criteria2Query. CT-BERT NER achieves 0.844 F1, while Att-BiLSTM and Criteria2Query achieve 0.802 F1 and 0.804 F1, respectively. For relation extraction, the CT-BERT rule-based model achieves 0.870 F1, while the equivalent model in Criteria2Query only achieves 0.793 F1. The CT-BERT rule-based model also outperforms the machine learning models trained on a curated relation data set. This suggests the advantage of the rule-based method in relation extraction. Conclusions The NER and relation extraction results demonstrate the effectiveness of CT-BERT for clinical trial information extraction. These results form the foundation for advancing clinical trials NLP towards AI-enhanced clinical trial design.

show abstract

EMR2vec: Bridging the gap between patient data and clinical trial

Cited by 17 publications

References 40 publications

A review of research on eligibility criteria for clinical trials

A review of research on eligibility criteria for clinical trials

Optimizing deep neural networks to predict the effect of social distancing on COVID-19 spread

Clinical Trial Information Extraction with BERT

Contact Info

Product

Resources

About