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A human–computer collaborative approach to identifying common data elements in clinical trial eligibility criteria

Zhihui Luo, Riccardo Miotto, Chunhua Weng
2013 Journal of Biomedical Informatics  
Design: A set of free-text eligibility criteria from clinical trials on two representative diseases, breast cancer and cardiovascular diseases, was sampled to identify disease-specific eligibility criteria  ...  Conclusion: It is feasible and effort saving to use a human-computer collaborative approach to augment domain experts for identifying disease-specific CDEs from free-text clinical trial eligibility criteria  ...  The benefits of using CDEs for clinical trial eligibility criteria in trial search are well recognized.  ... 
doi:10.1016/j.jbi.2012.07.006 pmid:22846169 pmcid:PMC3524400 fatcat:o5okpi3n65f45i6gydjt5gjk64

Expanding the Diversity of Texts and Applications: Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook

Aurélie Névéol, Pierre Zweigenbaum
2018 IMIA Yearbook of Medical Informatics  
Bibliographic databases PubMed and Association of Computational Linguistics (ACL) Anthology were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome  ...  They draw from text genres as diverse as clinical narratives across hospitals and languages or social media.  ...  It used the free text keywords "medical", "clinical", and "health".  ... 
doi:10.1055/s-0038-1667080 pmid:30157523 fatcat:26pmh3ofuvbjde2qhzrmfat54e

eTACTS: A method for dynamically filtering clinical trial search results

Riccardo Miotto, Silis Jiang, Chunhua Weng
2013 Journal of Biomedical Informatics  
Materials and methods: eTACTS mines frequent eligibility tags from free-text clinical trial eligibility criteria and uses these tags for trial indexing.  ...  We present eTACTS, a novel interactive retrieval framework using common eligibility tags to dynamically filter clinical trial search results.  ...  Acknowledgments The authors would like to thank the reviewers for their constructive and insightful comments as well as all the study participants in the user evaluations.  ... 
doi:10.1016/j.jbi.2013.07.014 pmid:23916863 pmcid:PMC3843999 fatcat:wty32nxuijgfrewwat6dt3voxe

Automated determination of metastases in unstructured radiology reports for eligibility screening in oncology clinical trials

Valentina I Petkov, Lynne T Penberthy, Bassam A Dahman, Andrew Poklepovic, Chris W Gillam, James H McDermott
2013 Experimental biology and medicine  
One approach to meet these accrual challenges is to utilize technology to automatically screen patients for clinical trial eligibility.  ...  This manuscript reports on the evaluation of different automated approaches to determine the metastatic status from unstructured radiology reports using the Clinical Trials Eligibility Database Integrated  ...  Acknowledgment Services in support of the research project were provided by the VCU Cancer Research Informatics and Services Shared Resource, supported, in part, with the funding from NIH-NCI Cancer Center  ... 
doi:10.1177/1535370213508172 pmid:24108448 pmcid:PMC4358809 fatcat:hsv3fdnupvfgvoz77dukoixkoa

Automated matching software for clinical trials eligibility: Measuring efficiency and flexibility

Lynne Penberthy, Richard Brown, Federico Puma, Bassam Dahman
2010 Contemporary Clinical Trials  
have an opportunity to be evaluated for participation in clinical trials as appropriate.  ...  across 5 different clinical trials and clinical trial scenarios.  ...  Acknowledgments Statement of Funding: This project was supported by the Massey Cancer Center.  ... 
doi:10.1016/j.cct.2010.03.005 pmid:20230913 pmcid:PMC4387843 fatcat:z4jsbgb5kbakxiiphh55swmv7i

Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices

Shruti Rao, Beth Pitel, Alex H. Wagner, Simina M. Boca, Matthew McCoy, Ian King, Samir Gupta, Ben Ho Park, Jeremy L. Warner, James Chen, Peter K. Rogan, Debyani Chakravarty (+3 others)
2020 JCO Clinical Cancer Informatics  
interpretations of complex genomic results for each patient, within an institution or hospital network.  ...  Yet the clinical cancer genomics field has been hindered by redundant efforts to meaningfully collect and interpret disparate data types from multiple high-throughput modalities and integrate into clinical  ...  This has led to a number of new trial designs. 59 Umbrella trials, such as the I-SPY2 trial in breast cancer 9 and the LUNG-MAP trial in lung cancer, 60 use a master protocol for a single tumor tissue  ... 
doi:10.1200/cci.19.00169 pmid:32644817 pmcid:PMC7397775 fatcat:6hwpu2yivfacnfj2fthnp7akwu

Building a Library of Eligibility Criteria to Support Design of Clinical Trials [chapter]

Krystyna Milian, Anca Bucur, Frank van Harmelen
2012 Lecture Notes in Computer Science  
The completion of clinical trial depends on sufficient participant enrollment, which is often difficult due to the restrictiveness of eligibility criteria, and effort required to verify patient eligibility  ...  The paper presents the first steps, a method for automatic comparison of criteria content and the library of structured and ordered eligibility criteria that can be browsed with the fine-grained queries  ...  The consequence of using this relaxation would be inclusion of patients that obtained such treatment for another purpose than breast cancer.  ... 
doi:10.1007/978-3-642-33876-2_29 fatcat:k7syaqnfkregpcx5vzpppjvn5a

Evidence-Based Clinical Guidelines in SemanticCT [chapter]

Qing Hu, Zhisheng Huang, Frank van Harmelen, Annette ten Teije, Jinguang Gu
2014 Communications in Computer and Information Science  
That lightweight formalisation of clinical guidelines have been integrated with SemanticCT, a semantically-enabled system for clinical trials.  ...  We show how they are useful in SemanticCT for the applications of the Semantic Web technology in medical domains.  ...  Those knowledge services include i) Semantic search for SPAQR queries over LarKC SPARQL endpoints; ii) Automatic patient recruitment by fast identification of eligible patients; iii) Rule-based reasoning  ... 
doi:10.1007/978-3-662-45495-4_18 fatcat:nky7a7xidndavgudendoiqnyai

Learning Eligibility in Cancer Clinical Trials Using Deep Neural Networks

Aurelia Bustos, Antonio Pertusa
2018 Applied Sciences  
We used protocols from cancer clinical trials that were available in public registries from the last 18 years to train word-embeddings, and we constructed a dataset of 6M short free-texts labeled as eligible  ...  We show that representation learning using deep neural networks can be successfully leveraged to extract the medical knowledge from clinical trial protocols for potentially assisting practitioners when  ...  Acknowledgment This work was supported by Medbravo, the Pattern Recognition and Artificial Intelligence Group (GRFIA) and the University Institute for Computing Research (IUII) from the University of Alicante  ... 
doi:10.3390/app8071206 fatcat:l4myvrgbavbhvn5ld4gzkrjosm

GENERATOR Breast DataMart—The Novel Breast Cancer Data Discovery System for Research and Monitoring: Preliminary Results and Future Perspectives

Fabio Marazzi, Luca Tagliaferri, Valeria Masiello, Francesca Moschella, Giuseppe Ferdinando Colloca, Barbara Corvari, Alejandro Martin Sanchez, Nikola Dino Capocchiano, Roberta Pastorino, Chiara Iacomini, Jacopo Lenkowicz, Carlotta Masciocchi (+5 others)
2021 Journal of Personalized Medicine  
An AI-based process automatically extracts data from different sources and uses them for generating trend studies and clinical evidence.  ...  Conclusions: GENERATOR Breast DataMart was created for supporting breast cancer pathways of care.  ...  However, the GENERATOR Breast DataMart does not want to replace these already established systems, but rather offers the possibility to search for data sources automatically for any type of analysis and  ... 
doi:10.3390/jpm11020065 pmid:33498985 fatcat:kgly2byy4zcjjjr4qs2qdjvkyu

Clinical decision support for genetically guided personalized medicine: a systematic review

Brandon M Welch, Kensaku Kawamoto
2013 JAMIA Journal of the American Medical Informatics Association  
Objective To review the literature on clinical decision support (CDS) for genetically guided personalized medicine (GPM).  ...  Funding This study was funded by grant K01HG004645 from the US National Human Genome Research Institute, the University of Utah Department of Biomedical Informatics, and the University of Utah Program  ...  The funding sources played no role in the study design, in the collection, analysis and interpretation of data, in the writing of the manuscript, or in the decision to submit the manuscript for publication  ... 
doi:10.1136/amiajnl-2012-000892 pmid:22922173 pmcid:PMC3638177 fatcat:rih3u2ukzzfplb6trju5mupdyu

An information retrieval system for computerized patient records in the context of a daily hospital practice: the example of the Léon Bérard Cancer Center (France)

P. Biron, M. H. Metzger, C. Pezet, C. Sebban, E. Barthuet, T. Durand
2014 Applied Clinical Informatics  
Objectives: To describe the development and various uses of a tool for full-text search of computerized patient records. Methods: The technology is based on Solr, an open-source search engine.  ...  Conclusion: The project demonstrates that the introduction of full-text-search tools allowed practitioners to use unstructured medical information for various purposes.  ...  Tool for clinical research This tool also aids with identifying patients eligible for clinical trials, a process far quicker than manual search.  ... 
doi:10.4338/aci-2013-08-cr-0065 pmid:24734133 pmcid:PMC3974255 fatcat:44d6nbdknndfhfucnqfueoa6lm

Systematic identification of pharmacogenomics information from clinical trials

Jiao Li, Zhiyong Lu
2012 Journal of Biomedical Informatics  
Many clinical related questions may be asked such as 'what drug should be prescribed for a patient with mutant alleles?'  ...  trials than in their corresponding publications, suggesting that clinical trials may be valuable for both validating known and capturing new PGx related information in a more timely manner.  ...  The authors would also like to thank the PharmGKB team for clarifying their curation scope and discussing the usefulness of our work.  ... 
doi:10.1016/j.jbi.2012.04.005 pmid:22546622 pmcid:PMC3760158 fatcat:ysuy6elftjdepmgzmgpcse47xm

Engagement in an interactive app for self-management of symptoms among patients treated for breast and prostate cancer: results from two randomized controlled trials (Preprint)

Marie-Therése Crafoord, Maria Fjell, Kay Sundberg, Marie Nilsson, Ann Langius-Eklöf
2019 Journal of Medical Internet Research  
Most patients used the self-care advice and free text message component. Among the patients treated for breast cancer, higher age predicted a higher total number of free text messages sent (P = .04).  ...  Using mobile technology for symptom management and self-care can improve patient-clinician communication and clinical outcomes in patients with cancer.  ...  The authors would like to thank the patients who participated in this study and the health care professionals at the oncology clinics who assisted us.  ... 
doi:10.2196/17058 pmid:32663140 fatcat:5uj7geqtyzcjvnajy2wg6h5hua

NLP Algorithms Endowed for Automatic Extraction of Information from Unstructured Free-Text Reports of Radiology Monarchy

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Natural Language Processing (NLP) Algorithms are the key factors for automatic information extraction form the unstructured free-text radiology reports .To extract clinically important findings and recommendations  ...  A rule-based NLP system is used in most of the automated IE applications in medical domain; whereas some applications are using Random Forest classifier, PageRank Algorithm, clustering algorithm, Conditional  ...  various use cases including radiology patient prioritization, cohort generation for clinical research, eligibility screening for clinical trials, and assessing imaging utilization had studied.  ... 
doi:10.35940/ijitee.l8009.1091220 fatcat:sjth33dnvjfnhn442figt75llq
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