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| # | Article | DOI | year | description |
|---|---|---|---|---|
| 1 | Study of Pre-trained Language Models for Named Entity Recognition in Clinical Trial Eligibility Criteria from Multiple Corpora | 10.1109/ICHI52183.2021.00095 | 2021 | To explore transformer-based models for clinical term extraction from eligibility criteria |
| 2 | Accuracy of an Artificial Intelligence System for Cancer Clinical Trial Eligibility Screening: Retrospective Pilot Study | 10.2196/27767 | 2021 | To evaluate Watson CDSS system for matching patients to clinical trials |
| 3 | Extending the Query Language of a Data Warehouse for Patient Recruitment | 10.3233/978-1-61499-808-2-152 | 2017 | To develop a data warehouse querying language for matching patient electronic health records with clinical trial eligibility criteria |
| 4 | An Evaluation of Pretrained BERT Models for Comparing Semantic Similarity Across Unstructured Clinical Trial Texts | 10.3233/SHTI210848 | 2022 | To evaluate the efficacy of BERT models in assessing semantic similarity of clinical trial descriptions |
| 5 | Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations | 10.1093/jamia/ocw176 | 2017 | To develop automated classification methods for eligibility criteria to facilitate patient-trial matching for specific populations such as persons living with HIV or pregnant women. |
| 6 | A Unified Machine Reading Comprehension Framework for Cohort Selection | 10.1109/JBHI.2021.3095478 | 2022 | To develop a machine reading comprehension (MRC) framework for cohort selection and evaluate different MRC algorithms, in particular, the use of the cross-criteria attention. |
| 7 | ASCOT: A text mining-based web-service for efficient search and assisted creation of clinical trials | 10.1186/1472-6947-12-S1-S3 | 2012 | To develop an efficient search tool for filtering clinical trial descriptions. |
| 8 | Cohort selection for clinical trials using deep learning models | 10.1093/jamia/ocz139 | 2019 | To evaluate the efficiency of deep neural network architectures in cohort selection tasks. |
| 9 | EliIE: An open-source information extraction system for clinical trial eligibility criteria | 10.1093/jamia/ocx019 | 2017 | To develop a system for parsing free text clinical trial descriptions. |
| 10 | Classifying Eligibility Criteria in Clinical Trials Using Active Deep Learning | 10.1109/ICMLA.2018.00052 | 2018 | To automatically identify eligibility criteria that can be evaluated by patients without the help of medical professionals. |
| 11 | Protocol Feasibility Workflow Using an Automated Multi-country Patient Cohort System | 10.3233/978-1-61499-432-9-985 | 2014 | To build a system for querying patients eligible for clinical trial participation across many countries' registries. |
| 12 | Assessing clinical trial eligibility with logic expression queries | 10.1016/j.datak.2007.07.005 | 2008 | To extract semantic information reflecting eligibility criteria from clinical trial descriptions and formulate queries that can match criteria against medical data in patient records. |
| 13 | Improving the efficiency of clinical trial recruitment using electronic health record data, natural language processing, and machine learning | 10.1002/art.41108 | 2019 | To evaluate if ensemble machine learning algorithm can improve the efficiency of eligibility screening. |
| 14 | Automatic trial eligibility surveillance based on unstructured clinical data | 10.1016/j.ijmedinf.2019.05.018 | 2019 | To develop an algorithm for automatic identification of patients eligible for clinical trial participation. |
| 15 | Information Extraction from Free Text in Clinical Trials with Knowledge-based Distant Supervision | 10.1109/COMPSAC.2019.00158 | 2019 | To develop a method for extracting medical concepts from free text clinical trial descriptions. |
| 16 | ETACTS: A method for dynamically filtering clinical trial search results | 10.1016/j.jbi.2013.07.014 | 2013 | To evaluate the eTACTS system for advanced querying of clinical trial descriptions. |
| 17 | A practical method for transforming free-text eligibility criteria into computable criteria | 10.1016/j.jbi.2010.09.007 | 2011 | To develop a method of transforming free text eligibility criteria into a form suitable for SPARQL/SQL querying |
| 18 | Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients | 10.1093/jamiaopen/ooaa002 | 2020 | To evaluate the performance of IBM Watson for matching patients to clinical trials. |
| 19 | Increasing the efficiency of trial-patient matching: automated clinical trial eligibility Pre-screening for pediatric oncology patients | 10.1186/s12911-015-0149-3 | 2015 | To identify patients who meet core eligibility characteristics of an oncology clinical trial. |
| 20 | An eligibility criteria query language for heterogeneous data warehouses | 10.3414/ME13-02-0027 | 2015 | To develop a clinical-readable query language for heterogenous warehouses of medical information. |
| 21 | DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction | 10.1145/3366423.3380181 | 2020 | To develop a deep neural network for patient-trial matching using cross-modal representation of eligibility criteria and patient electronic health records. |
| 22 | Improving Clinical Trial Participant Prescreening With Artificial Intelligence (AI): A Comparison of the Results of AI-Assisted vs Standard Methods in 3 Oncology Trials | 10.1177/2168479018815454 | 2018 | To evaluate the efficacy of Mendel.ai in enrolling eligible patients in clinical trials. |
| 23 | ExaCT: automatic extraction of clinical trial characteristics from journal publications | 10.1186/1472-6947-10-56 | 2010 | To develop an information exctraction system for extracting key trial characteristics from full-text journal articles |
| 24 | Building a specialized lexicon for breast cancer clinical trial subject eligibility analysis | 10.1177/1460458221989392 | 2021 | To develop the specialized lexicon of medical terms. |
| 25 | An OMOP CDM-Based Relational Database of Clinical Research Eligibility Criteria | 10.3233/978-1-61499-830-3-950 | 2017 | To build a database of clinical trials for easy querying and filtering of trials using multiple criteria. |
| 26 | An Ensemble Learning Strategy for Eligibility Criteria Text Classification for Clinical Trial Recruitment: Algorithm Development and Validation | 10.2196/17832 | 2020 | To evaluate the efficacy of model ensembling in the task of eligibility criteria classification. |
| 27 | Chia, a large annotated corpus of clinical trial eligibility criteria | 10.1038/s41597-020-00620-0 | 2020 | To create a large annotated corpus of eligibility criteria extracted from clinical trial descriptions. |
| 28 | Cohort selection for clinical trials using multiple instance learning | 10.1016/j.jbi.2020.103438 | 2020 | To verify the usefulness of multiple instance learning paradigm in patient-trial matching task. |
| 29 | Learning Eligibility in Cancer Clinical Trials Using Deep Neural Networks | 10.3390/app8071206 | 2018 | To automatically predict whether short clinical statements were considered inclusion or exclusion criteria. |
| 30 | Valx: A System for Extracting and Structuring Numeric Lab Test Comparison Statements from Text | 10.3414/ME15-01-0112 | 2016 | To develop and evaluate an automated method for extracting and structuring numeric comparison statements in trial eligibility criteria text. |
| 31 | EMR2vec: Bridging the gap between patient data and clinical trial | 10.1016/j.cie.2021.107236 | 2021 | To develop a system for patient-trial matching. |
| 32 | How the clinical research community responded to the COVID-19 pandemic: an analysis of the COVID-19 clinical studies in ClinicalTrials.gov | 10.1093/jamiaopen/ooab032 | 2021 | To conduct a systematic analysis of clinical trials on COVID-19 |
| 33 | DQueST: dynamic questionnaire for search of clinical trials | 10.1093/jamia/ocz121 | 2019 | To develop a patient-trial matching system based on dynamic questionaire generation. |
| 34 | Conflict discovery and analysis for clinical trials | 10.1145/3079452.3079494 | 2017 | To match medical treatments to exclusion criteria in clinical trial text. |
| 35 | COVID-19 trial graph: a linked graph for COVID-19 clinical trials | 10.1093/jamia/ocab078 | 2021 | To create a graph database of structured and unstructured medical information fromm COVID-19 clinical trials |
| 36 | Identifying the status of genetic lesions in cancer clinical trial documents using machine learning | 10.1186/1471-2164-13-S8-S21 | 2012 | To develop a machine learning system for identifying mentions of genes and genetic lesions in cancer clinical trials. |
| 37 | Parsing clinical trial eligibility criteria for cohort query by a multi-input multi-output sequence labeling model | 10.1101/2021.11.18.21266533 | 2021 | To evaluate a sequence labeling model in parsing and extracting medical entities in eligibility criteria. |
| 38 | Transformer-Based Named Entity Recognition for Parsing Clinical Trial Eligibility Criteria | 10.1145/3459930.3469560 | 2021 | To extract named entities in eligibility criteria using transformer-based model. |
| 39 | Cohort Selection for Clinical Trials From Longitudinal Patient Records: Text Mining Approach | 10.2196/15980 | 2019 | To evaluate a system for patient-trial matching. |
| 40 | Medical knowledge infused convolutional neural networks for cohort selection in clinical trials | 10.1093/jamia/ocz128 | 2019 | To embed electronic health records using convolutional neural networks for better patient-trial matching. |
| 41 | Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods | 10.1186/s12911-021-01487-w | 2021 | To extract semantic categories from eligibility criteria in Chinese clinical trials. |
| 42 | Building an OMOP common data model-compliant annotated corpus for COVID-19 clinical trials | 10.1016/j.jbi.2021.103790 | 2021 | To build an annotated dataset of eligibility criteria for COVID-19 clinical trials. |
| 43 | Improving Disease Named Entity Recognition for Clinical Trial Matching | 10.1109/BIBM47256.2019.8983421 | 2019 | To develop a named entity recognition model for clinical trial texts. |
| 44 | Analysis of eligibility criteria representation in industry-standard clinical trial protocols | 10.1016/j.jbi.2013.06.001 | 2013 | To develop a method of standarization of eligibility criteria across different clinical trial databases. |
| 45 | Syntactic and Semantic Knowledge-Aware Paraphrase Detection for Clinical Data | 10.1007/978-981-16-2937-2_13 | 2022 | To develop a knowledge-aware neural network model for paraphrase detection in eligibility criteria texts. |
| 46 | Criteria2Query: A natural language interface to clinical databases for cohort definition | 10.1093/jamia/ocy178 | 2019 | To develop a pipeline for eligibility criteria parsing and converting into CDM-based cohort queries. |
| 47 | Textual inference for eligibility criteria resolution in clinical trials | 10.1016/j.jbi.2015.09.008 | 2015 | To build a dataset of clinical texts annotated with eligibility criteria entailment. |
| 48 | Inferring appropriate eligibility criteria in clinical trial protocols without labeled data | 10.1145/2390068.2390074 | 2012 | To develop an unsupervised method of eligibility criteria identification in clinical trial texts. |
| 49 | Formulating queries for assessing clinical trial eligibility | 10.1007/11765448_8 | 2006 | To extract semantic information from eligibility criteria for better patient-trial matching. |
| 50 | Numerical Eligibility Criteria in Clinical Protocols: Annotation, Automatic Detection and Interpretation | 10.1007/978-3-319-59758-4_22 | 2017 | To create a model for the detection of complex numerical eligibility criteria in clinical trial texts. |
| 51 | Automated learning of domain taxonomies from text using background knowledge | 10.1016/j.jbi.2016.09.002 | 2016 | To create a framework for unsupervised ontology learning from clinical trial texts. |
| 52 | Feasibility of Feature-based Indexing, Clustering, and Search of Clinical Trials | 10.3414/ME12-01-0092 | 2013 | To explore the feasibility of feature-based indexing, clustering, and search of clinical trials. |
| 53 | Predictive modeling of clinical trial terminations using feature engineering and embedding learning | 10.1038/s41598-021-82840-x | 2021 | To predict clinical trial terminations and identify main factors influencing the terminations. |
| 54 | Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department | 10.1136/amiajnl-2014-002887 | 2015 | To evaluate an automated eligibility screening approach to clinical trials using different machine learning methods. |
| 55 | Supporting Patient Screening to Identify Suitable Clinical Trials | 10.3233/978-1-61499-432-9-823 | 2014 | To develop a solution for flexible formalization of criteria and other trial metadata and for effective management of those representations |
| 56 | Attention-Based LSTM Network for COVID-19 Clinical Trial Parsing | 10.1109/BigData50022.2020.9378451 | 2020 | To investigate different versions of Att-BiLSTM models to extract entities from COVID-19 elgibility criteria |
| 57 | Patterns for conflict identification in clinical trial eligibility criteria | 10.1109/HealthCom.2016.7749519 | 2016 | To develop a method for automated identification of potential treatment conflicts between trials. |
| 58 | Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center | 10.1200/CCI.19.00079 | 2020 | To evaluate the performance of WCTM tool in patient data intake and matching porcesses. |
| 59 | Clinical Trial Information Extraction with BERT | 10.1109/ICHI52183.2021.00092 | 2021 | To evaluate the effectiveness of BERT embeddings in medical information extraction. |
| 60 | A knowledge base of clinical trial eligibility criteria | 10.1016/j.jbi.2021.103771 | 2021 | To build a standardized knowledge base of elgibility criteria. |
| 61 | Classification And Extraction Of Medical Clinical Trial Screening Standard Texts Based On Bi-Lstm And Attention Mechanism | 10.1088/1755-1315/632/5/052088 | 2021 | To develop a model for eligibility criteria classification. |
| 62 | Understanding Common Key Indicators Of Successful And Unsuccessful Cancer Drug Trials Using A Contrast Mining Framework On Clinicaltrials.Gov | 10.1016/j.jbi.2023.104321 | 2023 | To evaluate the effectiveness of contrastive learning paradigm in the prediction of clinical trial success. |
| 63 | A Comparative Study Of Pre-Trained Language Models For Named Entity Recognition In Clinical Trial Eligibility Criteria From Multiple Corpora | 10.1186/s12911-022-01967-7 | 2022 | To compare multiple pre-trained language models in the task of named entity recognition in clinical trial descriptions. |
| 64 | Improving Clinical Trial Design Using Interpretable Machine Learning Based Prediction Of Early Trial Termination | 10.1038/s41598-023-27416-7 | 2023 | To inform clinical trial design using interpretable machine learning models for predicting early trial terminations. |
| 65 | Combining Human And Machine Intelligence For Clinical Trial Eligibility Querying | 10.1093/jamia/ocac051 | 2022 | To develop a system (Criteria2Query 2.0) for automatic conversion of eligibility criteria into cohort queries. |
| 66 | A Scalable Ai Approach For Clinical Trial Cohort Optimization | 10.1007/978-3-030-93733-1_36 | 2021 | To inform clinical trial design by automatic extraction of eligibility criteria. |
| 67 | The Leaf Clinical Trials Corpus: A New Resource For Query Generation From Clinical Trial Eligibility Criteria | 10.1038/s41597-022-01521-0 | 2022 | To build an annotated corpus of clinical trial eligibility criteria. |
| 68 | Piloting An Automated Clinical Trial Eligibility Surveillance And Provider Alert System Based On Artificial Intelligence And Standard Data Models | 10.1186/s12874-023-01916-6 | 2023 | To improve patient-trial matching by electronic health record normalization and to build efficient user interface for clinical trial selection. |
| 69 | Hint: Hierarchical Interaction Network For Clinical-Trial-Outcome Predictions | 10.1016/j.patter.2022.100445 | 2022 | To predict the success of a clinical trial using deep neural networks. |
| 70 | Predicting Publication Of Clinical Trials Using Structured And Unstructured Data: Model Development And Validation Study | 10.2196/38859 | 2022 | To develop a system for clinical trial publication |
| 71 | Growth In Eligibility Criteria Content And Failure To Accrue Among National Cancer Institute (Nci)-Affiliated Clinical Trials | 10.1002/cam4.5276 | 2023 | To investigate the impact of eligibility criteria on trial accrual and identify criteria assosiated with accrual failure |
| 72 | Utilizing Large Language Models For Enhanced Clinical Trial Matching: A Study On Automation In Patient Screening | 10.7759/cureus.60044 | 2024 | To apply LLMs for automatic trial aligibility screening. |
| 73 | Autocriteria: A Generalizable Clinical Trial Eligibility Criteria Extraction System Powered By Large Language Models | 10.1093/jamia/ocad218 | 2024 | To build an LLM-based tool for information extraction frm eligibility criteria without a need of labeled training data. |
| 74 | Automatic Assessment Of Patient Eligibility By Utilizing Nlp And Rule-Based Analysis | 10.1109/EMBC40787.2023.10340494 | 2023 | To develop a model for fully automatic selection of patients for clinical trials. |
| 75 | Characterisation Of Digital Therapeutic Clinical Trials: A Systematic Review With Natural Language Processing | 10.1016/S2589-7500(23)00244-3 | 2024 | To explore digital therapeutics clinical trials. |
| 76 | Sociotechnical Feasibility Of Natural Language Processing-Driven Tools In Clinical Trial Eligibility Prescreening For Alzheimer'S Disease And Related Dementias | 10.1093/jamia/ocae032 | 2024 | To assess feasibility of existing NLP tools for eligibility prescreening for AD trials. |
| 77 | A Self-Learning Resource-Efficient Re-Ranking Method For Clinical Trials Search | 10.1145/3583780.3615174 | 2023 | To develop a CT ranking method to facilitate trial search. |
| 78 | Automated Matching Of Patients To Clinical Trials: A Patient-Centric Natural Language Processing Approach For Pediatric Leukemia | 10.1200/CCI.23.00009 | 2023 | To create a patient-trial matching tool for pediatric leukemia. |
| 79 | Effective Matching Of Patients To Clinical Trials Using Entity Extraction And Neural Re-Ranking | 10.1016/j.jbi.2023.104444 | 2023 | To develop a method for clinical trial retrieval for the purpose of patient-trial matching |
| 80 | Distilling Large Language Models For Matching Patients To Clinical Trials | 10.1093/jamia/ocae073 | 2024 | To compare the efficacy of proprietary vs. open-source LLMs in patient-trial matching task. |
| 81 | Named Entity Recognition and Normalization for Alzheimer's Disease Eligibility Criteria | 10.1109/ICHI57859.2023.00100 | 2023 | To develop a pipeline for information extraction from AD clinical trial eligibility criteria. |
| 82 | Criteria2Query 3.0: Leveraging Generative Large Language Models For Clinical Trial Eligibility Query Generation | 10.1016/j.jbi.2024.104649 | 2024 | To create a tool leveraging LLMs for information extraction from eligbility criteria. |
| 83 | Treement: Interpretable Patient-Trial Matching Via Personalized Dynamic Tree-Based Memory Network | 10.1145/3584371.3612998 | 2023 | To develop a model for interpretable patient trial matching. |