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14 results for “clinical notes”
Multi-head CRF classifier for biomedical multi-class Named Entity Recognition on Spanish clinical notes
<p>This contains the merged dataset as described in the work "<strong>Multi-head CRF classifier for biomedical multi-class Named Entity Recognition on Spanish clinical notes"</strong>.</p> <p>This dataset consists of 4 seperate datasets:</p> <ul> <li><a href="../records/8224056" target="_blank" rel="noopener">MedProcNer</a></li> <li><a href="../records/7614764" target="_blank" rel="noopener">DisTEMIST</a></li> <li><a href="../records/4270158" target="_blank" rel="noopener">PharmaCoNER</a></li> <li><a href="../records/10635215" target="_blank" rel="noopener">SympTEMIST</a></li> </ul> <p>The dataset contains two tasks:</p> <p><strong>Task 1:</strong> This task is related to multi-class Named Entity Recognition. This dataset contains 5 possible classes: SYMPTOM, PROCEDURE, DISEASE, CHEMICAL and PROTEIN.</p> <p><strong>Task 2:</strong> This task is related to Named Entity Linking, where each code corresponds to a code within the SNOMED-CT corpus. The exact corpus used can be obtained <a href="https://download.nlm.nih.gov/umls/kss/IHTSDO20190131/SnomedCT_SpanishRelease-es_PRODUCTION_20190430T120000Z.zip" target="_blank" rel="noopener">here</a>. Further for the MedProcNER, SympTEMIST and DisTEMIST datasets, a gazetteer is provided in the original datasets. </p> <p>For more information on the construction of the dataset, aswell as dataloaders, we refer you to our <a href="https://github.com/ieeta-pt/Multi-Head-CRF" target="_blank" rel="noopener">GitHub repository</a>.<br><br>Further this also contains the embeddings from the <a href="https://huggingface.co/cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR-large" target="_blank" rel="noopener">SapBERT</a> model.</p> <p><strong>Please, cite:</strong></p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <blockquote> <div>@article{jonker2024a, title = {Multi-head {{CRF}} classifier for biomedical multi-class named entity recognition on {{Spanish}} clinical notes}, author = {Jonker, Richard A. A. and Almeida, Tiago and Antunes, Rui and Almeida, Jo{\~a}o R. and Matos, S{\'e}rgio}, year = {2024}, journal = {Database}, publisher = {Oxford University Press} }</div> </blockquote> <div>Jonker, R. A. A., Almeida, T., Antunes, R., Almeida, J. R., & Matos, S. (2024). Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes. (Submitted.) </div> <div> </div> <div> <p><strong>License</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Extraction of clinical phenotypes for Alzheimer disease dementia from clinical notes using natural language processing
<p><strong>Objectives</strong></p> <p>There is much interest in utilizing clinical data for developing prediction models for Alzheimer disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured Electronic Health Record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR.</p> <p><strong>Materials and Methods</strong></p> <p>We developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by two clinical dementia experts for AD-related clinical phenotypes including medical comorbidities, biomarkers, neurobehavioral test scores, behavioral indicators of cognitive decline, family history, and neuroimaging findings.</p> <p><strong>Results</strong></p> <p>Documentation rates for each phenotype varied in the structured versus unstructured EHR. Inter-annotator agreement was high (Cohen's kappa = 0.72–1) and positively correlated with the NLP-based phenotype extraction pipeline's performance (average F1-score = 0.65-0.99) for each phenotype.</p> <p><strong>Discussion</strong></p> <p>We developed an automated NLP-based pipeline to extract informative phenotypes that may improve the performance of eventual machine-learning predictive models for AD. In the process, we examined documentation practices for each phenotype relevant to the care of AD patients and identified factors for success.</p> <p><strong>Conclusion</strong></p> <p>Success of our NLP-based phenotype extraction pipeline depended on domain-specific knowledge and focus on a specific clinical domain instead of maximizing generalizability. </p>
Extraction of clinical phenotypes for Alzheimer disease dementia from clinical notes using natural language processing
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Randomized Trial of an Ambient AI Scribe (Voa Health) That Converts Outpatient Visit Audio Into Draft Notes, Comparing Visits With vs. Without AI Across Multiple Clinics to Assess Physician Well-being
ClinicalTrials.gov study NCT07302906. IPD Sharing: YES. Countries: 0. Publications: 5.
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review
<p>Supplementary material outlining complete list of reviewed papers, chronic diseases and their classifications, algorithms used, publication venues, and excluded papers.</p>
Relisten: Enhancing Care Quality and Saving Time With Automatic Clinical Note Generation in Primary Care
ClinicalTrials.gov study NCT06618092. IPD Sharing: NO. Countries: 1. Publications: 1.
External Lid Loading for the Temporary Treatment of the Paresis of the M. Orbicularis Oculi: a Clinical Note
ClinicalTrials.gov study NCT01274689. IPD Sharing: Not stated. Countries: 1. Publications: 1.
IMTN Study- International Prospective Multicenter Trial on Clinical NOTES
ClinicalTrials.gov study NCT00995735. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Trial to Improve Family Clinical Note Access and Outcomes for Hospitalized Children
ClinicalTrials.gov study NCT06722378. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Online Access to Clinical Treatment Notes for Outpatients
ClinicalTrials.gov study NCT07135726. IPD Sharing: NO. Countries: 0. Publications: 2.
Transvaginal NOTES Cholecystectomy: Phase I/II Mexico Clinical Trial
ClinicalTrials.gov study NCT00710502. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Adaptation and Validation of a Score From English to Spanish for Evaluating Quality of Clinical Notes
ClinicalTrials.gov study NCT01836107. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Ambient Digital Scribes Populating Clinical Notes
ClinicalTrials.gov study NCT06519123. IPD Sharing: NO. Countries: 1. Publications: 0.
A Clinical Trial to Evaluate Effect of IAE0972 Combined with Chemotherapy for R/M HNSCC or NPC(Note: It is Currently Phase II.).
ClinicalTrials.gov study NCT06719479. IPD Sharing: NO. Countries: 0. Publications: 0.
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OpenNeuro
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