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222 results for “clinical standard”
CodiEsp corpus: gold standard Spanish clinical cases coded in ICD10 (CIE10) - eHealth CLEF2020
<p><strong>Introduction</strong></p> <p>These are the train, development and test sets of the CodiEsp corpus. Train, development and test have gold standard annotations. In addition, the unannotated background set is also distributed. All documents are released in the context of the CodiEsp track for CLEF ehealth 2020 (<a href="http://temu.bsc.es/codiesp/">http://temu.bsc.es/codiesp/</a>).</p> <p>The CodiEsp corpus contains manually coded clinical cases. All documents are in Spanish language and CIE10 is the coding terminology (it is the Spanish version of ICD10-CM and ICD10-PCS). The CodiEsp corpus has been randomly sampled into three subsets: the train, the development, and the test set. The train set contains 500 clinical cases, and the development and test set 250 clinical cases each. CodiEsp participants must submit predictions for the test and background set, but they will only be evaluated on the test set.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020</p> <pre><code>@inproceedings{miranda2020overview, title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2020} }</code></pre> <p> </p> <p><strong>Annotation quality</strong></p> <p>Inter-annotator agreement: 88.6% for diagnosis coding, 88.9% for procedure coding and 80.5% for the textual reference annotation. For more information, see the <a href="http://ceur-ws.org/Vol-2696/paper_263.pdf">paper</a>.</p> <p><br> <strong>Zip structure</strong><br> Four folders: train, dev, test and background. Each one of them contains the files for the train, development, test and background corpora, respectively.</p> <ul> <li><strong>train, dev and test</strong> folders have: <ul> <li>3 tab-separated files with the annotation information relevant for each of the 3 sub-tracks of CodiEsp. </li> <li>A subfolder named <em>text_files</em> with the plain text files of the clinical cases.</li> <li>A subfolder named <em>text_files_en</em> with the plain text files machine-translated to English. Due to the translation process, the text files are sentence-splitted.</li> </ul> </li> <li>The <strong>background</strong> folder has only <em>text_files</em> and <em>text_files_en</em> subfolders with the plain text files.</li> </ul> <p><br> <strong>Format</strong><br> The CodiEsp corpus is distributed in plain text in UTF8 encoding, where each clinical case is stored as a single file whose name is the clinical case identifier. Annotations are released in a tab-separated file. Since the CodiEsp track has 3 sub-tracks, every set of documents (train and test) has 3 tab-separated files associated with it. </p> <p>For the sub-tracks CodiEsp-D and CodiEsp-P, the file has the following fields:</p> <pre>articleID ICD10-code </pre> <p>Tab-separated files for the sub-track CodiEsp-X contain extra fields that provide the text-reference and its position:</p> <pre>articleID label ICD10-code text-reference reference-position</pre> <p><br> <strong>Corpus summary statistics</strong><br> The final collection of 1000 clinical cases that make up the corpus had a total of 16504 sentences, with an average of 16.5 sentences per clinical case. It contains a total of 396,988 words, with an average of 396.2 words per clinical case.</p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/codiesp/">Web</a></strong></li> <li><strong><a href="http://ceur-ws.org/Vol-2696/paper_263.pdf">Citation</a>: </strong>Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020</li> <li><strong><a href="https://doi.org/10.5281/zenodo.3859869">Silver Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3730566">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhA0crlSVCYMPqMUWd4mXc4x"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/codiesp/index.php/participants-systems/"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>For more information, visit the track webpage: http://temu.bsc.es/codiesp/ or email us at encargo-pln-life@bsc.es</p> <p> </p> <p>Copyright (c) 2019 Secretaría de Estado para el Avance Digital</p>
Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard - Dataset
<p>Dataset of a cohort whose summary is described below.</p> <p>Abstract</p> <p><strong>Objective:</strong> To evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard. <strong>Method: </strong>Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong <em>versus</em> weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent). <strong>Results:</strong> With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5–99.5%) sensitivity, 70% (95% CI 65.8–74.2%) specificity, 85.5% (95% CI 83.4–87.7%) accuracy, PPV of 79.7% (95% CI 76.6–82.7%) and NPV of 97.6% (95% CI 95.9–99.2%). <strong>Conclusion: </strong>The proposed set of clinical-radiological criteria were accurate in identifying patients with strong <em>versus</em> weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.</p>
Gold standard annotation of epilepsy clinic letters for the development of information extraction tools
<p>This folder contains 200 synthetic letters, based on hospital outpatient epilepsy clinic consultations, written by neurology consultants, specialist trainees, and epilepsy specialist nurses. The letters were double annotated by trained researchers, according to annotation guidelines, uploaded separately (What and How of annotating with Markup). The 200 .ann annotation files are also included.</p> <p>We used Markup (https://getmarkup.com) for annotation (the configuration file is within the uploaded set of annotations) with an epilepsy concept list based on the Unified Medical Language System (UMLS) ontology. All annotations were compared, reviewed, and corrected to form a gold standard annotation set.</p> <p> </p>
PharmaCoNER corpus: gold standard annotations of Pharmacological Substances, Compounds and proteins in Spanish clinical case reports
<p><strong>Intro:</strong></p><p>The PharmaCoNER corpus (divided into train, dev and test) is a Gold Standard manually annotated dataset used for the the PharmaCoNER shared task posed at BIONLP-ST (at EMNLP). In addition, we include here the PharmaCoNER background set. It contains the train, development and test sets of the two subtasks (subtask-1 and subtask-2) with Gold Standard annotations. In addition, it contains the documents of the background set, without annotations.</p><p>The PharmaCoNER corpus consists of:</p><ul><li>Manually classified clinical case sections derived from Open access Spanish medical publications, named the Spanish Clinical Case Corpus (SPACCC).</li><li>It was manually selected by a practicing oncologist and revised by a clinical documentalist to assure that records were relevant/representative and resembled structure and content relevant to process clinical records.</li><li>The final corpus: 1000 clinical cases 16,504 sentences</li><li>The corpus contains a total of 396,988 words, with an average of 396.2 words per clinical case.</li><li>It covers a range of medical disciplines including oncology, urology, cardiology, pneumology or infections diseases, etc.</li><li>The corpus has been annotated at the mention level by experts in medicinal chemistry and pharmacology following a granular annotation scheme covering four mention types:<ul><li><i>Entity type 1 (NORMALIZABLES)</i>: mentions of chemicals that can be manually normalized to a unique concept identifier (primarily SNOMED-CT).</li><li><i>Entity type 2 (NO_NORMALIZABLES)</i>: mentions of chemicals that could not be normalized manually to a unique concept identifier.</li><li><i>Entity type 3 (PROTEINAS)</i>: mentions of proteins/genes following an adaptation of the BioCreative GPRO track annotation guidelines (includes peptides, peptide hormones & antibodies).</li><li><i>Entity type 4 (UNCLEAR )</i>: cases of general substance class mentions of clinical relevance, including certain pharmaceutical formulations, general treatments, chemotherapy programs, and vaccines.</li><li>Mentions class "<i>UNCLEAR</i>" (not evaluated for the PharmaCoNER track)</li></ul></li></ul><p> </p><p> </p><p><strong>Please, cite: </strong></p><p>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</p><p> </p><p><strong>Annotation quality</strong></p><p>Inter-annotator agreement: 93% for annotation, 73% for mapping.</p><p>For more information, see the <a href="https://paperswithcode.com/paper/pharmaconer-pharmacological-substances">paper</a>.</p><p> </p><p><strong>Format</strong></p><p>For subtask 1 annotations are distributed in <i>Brat</i> format. (More info at Brat webpage https://brat.nlplab.org/standoff.html)</p><p>For subtask-2, codes are associated with each document are given in a <i>TSV</i> file with the following columns: </p><blockquote><p>filename code</p></blockquote><p> </p><p><strong>Shared task goal:</strong></p><p>In the two subtasks, the goal is to predict the annotations of the test files (either the ANN files or the TSV with the codes) given only the plain text files. </p><p> </p><p><strong>Resources:</strong></p><ul><li><a href="https://temu.bsc.es/pharmaconer/"><strong>Web</strong></a></li><li><a href="https://www.aclweb.org/anthology/D19-5701.pdf"><strong>Citation</strong></a><strong>: </strong>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</li><li><a href="https://doi.org/10.5281/zenodo.4271908"><strong>Silver Standard corpus</strong></a></li><li><a href="https://doi.org/10.5281/zenodo.3763276"><strong>Annotation guidelines</strong></a></li><li><a href="https://github.com/TeMU-BSC/PharmaCoNER-Tagger"><strong>PharmaCoNER tagger</strong></a></li><li><a href="https://www.youtube.com/watch?v=B3ZzJl5OMkY"><strong>Youtube video(general setting)</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/pharmaconer-pharmacological-substances-compounds-and-proteins-named-entity-recognition-track-at-bionlpost-workshop-november-4-skycity-rm-2-hong-kong-emnlp2019"><strong>Slides PharmacoNER overview talk at BIONLP-ST / EMNLP </strong></a></li></ul><p>For further information, please visit <a href="https://temu.bsc.es/pharmaconer/">https://temu.bsc.es/pharmaconer/</a> or email us at encargo-pln-life@bsc.es</p><p>Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)</p><p> </p><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><p> </p><p><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p><br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in PharmaCoNER, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8413866">SympTEMIST</a> (Corpus of symptoms, signs and findings mentions and normalization, same document collection)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li><li><a href="https://zenodo.org/records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li></ul>
MEDDOCAN corpus: gold standard annotations for Medical Document Anonymization on Spanish clinical case reports
<p><strong>Intro:</strong></p> <p>Meddocan shared task dataset (divided in train, dev and test). In addition, we include here the Meddocan background set.</p> <p>It contains the training, development and test sets of the Meddocan shared task with Gold Standard annotations.</p> <p>In addition, it contains the documents of the background set, without annotations.</p> <p> </p> <p><strong>Annotation quality</strong></p> <p>Inter-annotator agreement: 98% </p> <p>For more information, see the <a href="http://ceur-ws.org/Vol-2421/MEDDOCAN_overview.pdf">paper</a>. </p> <p> </p> <p><strong>Format:</strong></p> <p>Annotations are distributed in Brat format. See <a href="https://brat.nlplab.org/standoff.html">Brat webpage</a> for more information.</p> <p>In addition, annotations are also distributed in XML format (based on i2b2 XML format).</p> <p>In the <a href="https://temu.bsc.es/meddocan/index.php/resources/">Meddocan webpage</a>, there is a script to convert between MEDDOCAN-Brat, MEDDOCAN-XML, and i2b2 formats.</p> <p> </p> <p><strong>Shared task goal:</strong></p> <p>In the three subtasks, the goal will be to predict the annotations given only the plain text files. </p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/meddocan/">Web</a></strong></li> <li><strong>Citation: </strong>Montserrat Marimon et al. “Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results.” In: IberLEF@ SEPLN. 2019, pp. 618–638.</li> <li><strong>Silver Standard corpus</strong></li> <li><a href="https://doi.org/10.5281/zenodo.4279337"><strong>Annotation guidelines</strong></a></li> </ul> <p> </p> <p>For further information, please visit <a href="https://temu.bsc.es/meddocan/">https://temu.bsc.es/meddocan/</a> or email us at encargo-pln-life@bsc.es</p> <p>Copyright (c) 2019 Secretaría de Estado para el Avance Digital (SEAD)</p>
OMOP2OBO: Semantic Integration of Standardized Clinical Terminologies to Power Translational Digital Medicine Across Health Systems (Recorded Introduction)
<p>This entry contains the recorded introduction that was presented at the 2020 Observational Health Data Science Initiative Symposium (<a href="https://www.ohdsi.org/events/2020-ohdsi-symposium/">https://www.ohdsi.org/events/2020-ohdsi-symposium/</a>).</p>
MOTU data. FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients.
<h3>Dataset presented in the article "MOTU on FHIR: A 10-year data collection on the clinical rehabilitation pathway of 1006 trans-femoral amputees".</h3> <p>Data has been anonymised prior the publication. The data are in Comma-separated-values (CSV) format. </p> <p>This work has been conducted within the framework of the MOTU++ project (PR19-PAI-P2).</p> <p><span>This research was co-funded by the Complementary National Plan PNC-I.1 "Research initiatives for innovative technologies and pathways in the health and welfare sector” D.D. 931 of 06/06/2022, DARE - DigitAl lifelong pRevEntion initiative, code PNC0000002, CUP: (B53C22006450001) and by the Italian National Institute for Insurance against Accidents at Work (INAIL) within the MOTU++ project (PR19-PAI-P2). </span></p> <p><span>Authors express their gratitude to all the AlmaHealthDB Team.</span></p>
FHIRed MOTU data. FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients.
<h3>Dataset presented in the article "MOTU on FHIR: A 10-year data collection on the clinical rehabilitation pathway of 1006 trans-femoral amputees".</h3> <p>Data has been anonymised prior the publication. The data has been standardized in Fast Healthcare Interoperability Resources (FHIR) data standard. </p> <p>This work has been conducted within the framework of the MOTU++ project (PR19-PAI-P2).</p> <p>This research was co-funded by the Complementary National Plan PNC-I.1 "Research initiatives for innovative technologies and pathways in the health and welfare sector” D.D. 931 of 06/06/2022, DARE - DigitAl lifelong pRevEntion initiative, code PNC0000002, CUP: (B53C22006450001) and by the Italian National Institute for Insurance against Accidents at Work (INAIL) within the MOTU++ project (PR19-PAI-P2).</p> <p>Authors express their gratitude to all the AlmaHealthDB Team.</p> <h2>Instruction MOTU-to-FHIR Importer</h2> <div> <div> <p>The repository includes a Docker Compose setup for importing the MOTU dataset into a HAPI FHIR server, formatted as NDJSON following the HL7 FHIR R4 standards.</p> <h3>Prerequisites</h3> <p>Before you begin, ensure you have the following installed:</p> <ul> <li><a href="https://www.docker.com/get-started/">Docker </a></li> <li><a href="https://docs.docker.com/compose/install/">Docker Compose</a></li> <li><a href="https://www.python.org/downloads/">Python >=3.7</a> </li> <li><a href="https://pypi.org/project/requests/">Requests Python Library</a></li> </ul> </div> <div> <h3>How to run</h3> <ol> <li>First, unzip the <code>dataset</code> directory containing the NDJSON files.</li> <li>Open a terminal or command prompt in the root directory of this repository.</li> <li>Run the command <code>docker-compose up</code> in the terminal to start the Docker containers.</li> <li>Once the containers are up and running, open another terminal window in the root directory of this repository.</li> <li>Run the command <code>python main.py</code> in the terminal to start the data import process.</li> <li>After the import process is complete, you can access the HAPI FHIR server by opening a web browser and navigating to <a href="http://localhost:8082" target="_blank" rel="nofollow noreferrer noopener">http://localhost:8082</a>.</li> </ol> <p> </p> </div> </div>
Training, executive, attention and motor skills (TEAMS) training versus standard treatment for preschool children with attention deficit hyperactivity disorder: a randomised clinical trial
<p>This is the dataset used in the trial entitled Training, executive, attention and motor skills (TEAMS) training versus standard treatment for preschool children with attention deficit hyperactivity disorder, published in BMC Research Notes.</p>
MEDDOPROF Silver Standard: Predictions for Occupations, Working Statuses and Activities in Clinical Case Reports
<p><strong>MEDDOPROF Silver Standard</strong></p> <p>This repository contains the <strong>MEDDOPROF Silver Standard</strong>, a collection of clinical case reports of multiple specialties that have been automatically annotated with occupations, working statuses and activities using named entity recognition models trained on the MEDDOPROF corpus. MEDDOPROF and this Silver Standard have been created by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis.</p> <p><strong>About the MEDDOPROF Corpus</strong></p> <p><a href="https://temu.bsc.es/meddoprof/">MEDDOPROF</a> is a corpus and shared task focused on the detection and normalization of occupations and related information such as working statuses and non-paid activities in clinical texts in Spanish. All mentions in the corpus have been normalized to SNOMED CT, with occupations also being normalized to the European Skills, Competences, and Occupations classification ESCO. MEDDOPLACE was released as part of the IberLEF 2021 workshop, which in turn was held within the SEPLN 2021 conference.</p> <p><strong>About this Silver Standard</strong></p> <p>The Silver Standard includes 13,357 documents and a total of 5,568,844 tokens. The documents belong to over 10 different medical specialties, including occupational health, oncology, radiology, infectology, dermatology, psichiatry and more.</p> <p>The corpus includes three different labels: PROFESION (occupation), SITUACION-LABORAL (working status) and ACTIVIDAD (activity/hobby). There is a total of 4,332 predictions (1,325 unique), with the most common label being PROFESION with 3,599 predictions (1,181 unique), followed by SITUACION-LABORAL with 682 (180 unique) and ACTIVIDAD with 51 (44 unique).</p> <p>The annotations are presented in .ann format, which is the format used by the annotation tool brat.</p> <p>For more information, please check the attached README file.</p> <p><strong>Citation</strong></p> <p>If you use this Silver Standard or any MEDDOPROF materials, please cite the task's overview paper:<br> Lima-López, Salvador, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias, & Martin Krallinger. “NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts.” Procesamiento del Lenguaje Natural [Online], 67 (2021): 243-256.</p> <p><strong>License</strong><br> This work is licensed under a Creative Commons Attribution 4.0 International License.</p> <p><strong>Contact</strong><br> If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br> - Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Acknowledgements</strong><br> MEDDOPROF was promoted by the Spanish government's 2020 Proyectos de I+D+i RTI Tipo A (AI4ProfHealth - DESCIFRANDO EL PAPEL DE LAS PROFESIONES EN LA SALUD DE LOS PACIENTES A TRAVES DE LA MINERIA DE TEXTOS (PID2020-119266RA-I00)).</p> <p><strong>Related Links</strong></p> <p>- MEDDOPROF website: <a href="https://temu.bsc.es/meddoprof/">https://temu.bsc.es/meddoprof/</a></p> <p>- MEDDOPROF Gold Standard: <a href="https://doi.org/10.5281/zenodo.5070540">https://doi.org/10.5281/zenodo.5070540</a></p> <p>- MEDDOPROF Annotation Guidelines: <a href="https://doi.org/10.5281/zenodo.4694675">https://doi.org/10.5281/zenodo.4694675</a></p>
MedProcNER Corpus: Gold Standard annotations for Clinical Procedures Information Extraction
<p><strong>MedProcNER</strong> stands for MEDical PROCedure Named Entity Recognition. It is a shared task and set of resources focused on the <strong>detection, normalization and indexing of clinical procedures in medical documents</strong> in <strong>Spanish</strong>. MedProcNER is complementary to the DisTEMIST corpus (https://temu.bsc.es/distemist) as they both use the same document collection.</p><p><strong>Please cite if you use this dataset:</strong></p><p>Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023.</p><p>@article{lima2023overview, title={Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023}, author={Lima-L{\'o}pez, Salvador and Farr{\'e}-Maduell, Eul{\`a}lia and Gasc{\'o}, Luis and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras, Georgios and Paliouras, Georgios and Krallinger, Martin}, journal={Working Notes of CLEF}, year={2023} }</p><p>This repository includes the Train Set of the task, which includes a total of 750 documents, plus the annotated Test Set's 250 documents. A gazetteer of possible <strong>SNOMED CT codes</strong> for the normalization and indexing tasks is also part of the bundle as a lexical resource. </p><p>In addition, a cross-mapping file of all SNOMED CT codes to <strong>MeSH</strong> is also included.</p><p>Finally, we release an experimental <strong>multilingual Silver Standard</strong> version derived from the Spanish Gold Standard in <strong>9 languages</strong>: <i><strong>English</strong></i>, <i><strong>Catalan</strong></i>, <i><strong>Italian</strong></i>, <i><strong>French</strong></i>, <i><strong>Portuguese</strong></i>, <i><strong>Romanian</strong></i>, <i><strong>Czech</strong></i>, <i><strong>Dutch</strong></i> and <i><strong>Swedish</strong></i>. </p><p>These documents have been generated using an automatic annotation transfer process that works as follows:</p><ol><li>The text files were translated with a neural machine translation system.</li><li>The annotations were translated with the same neural machine translation system.</li><li>The translated annotations were transferred to the translated text files using a lexical approach and custom dictionaries.</li></ol><p>MedProcNER was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioASQ @ CLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: <a href="https://temu.bsc.es/medprocner">https://temu.bsc.es/medprocner</a>.</p><p> </p><p><strong>Resources:</strong></p><ul><li><a href="https://temu.bsc.es/medprocner"><strong>Web</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/paper-002.pdf"><strong>Citation</strong></a><strong>: </strong>Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023.</li><li><a href="https://doi.org/10.5281/zenodo.7817666"><strong>Annotation guidelines</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/"><strong>Proceedings and participant papers</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/paper-002.pdf"><strong>Overview paper</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/medprocnerproctemist-shared-task-on-clinical-procedure-detection-and-normalization-task-overview-presentation-at-bioasq-workshop-of-clef-2023-conference"><strong>Overview talk slides at BioASQ/CLEF</strong></a></li></ul><p> </p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in MedProcNER, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8413866">SympTEMIST </a>(Corpus of symptoms, signs and findings mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, modified synthetic verions of the document collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li><li><a href="https://zenodo.org/records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li></ul><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><p><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p>
Prospective Trial to Compare the Clinical Efficiency of G-EYE™ HD Colonoscopy With Standard HD Colonoscopy
ClinicalTrials.gov study NCT01917513. IPD Sharing: Not stated. Countries: 8. Publications: 1.
A Clinical Trial Testing The Efficacy Of Crizotinib Versus Standard Chemotherapy Pemetrexed Plus Cisplatin Or Carboplatin In Patients With ALK Positive Non Squamous Cancer Of The Lung
ClinicalTrials.gov study NCT01154140. IPD Sharing: Not stated. Countries: 31. Publications: 7.
Comparing Biomarker-Guided DBS Programming With Standard Clinical Monopolar Programming
ClinicalTrials.gov study NCT07369310. IPD Sharing: NO. Countries: 1. Publications: 3.
A Clinical Trial Comparing Cangrelor to Clopidogrel Standard Therapy in Subjects Who Require Percutaneous Coronary Intervention (PCI) (CHAMPION PHOENIX)
ClinicalTrials.gov study NCT01156571. IPD Sharing: Not stated. Countries: 1. Publications: 21.
Clinical Trial of Standard Versus Goal-Directed Perioperative Fluid Management (GDT) for Patients Undergoing Liver Resection
ClinicalTrials.gov study NCT01596283. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Clinical Standardization Study of the Preferred Acupuncture Treatment Protocol to Treat Functional Constipation
ClinicalTrials.gov study NCT00508482. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Multi-Center Trial of High-resolution Transrectal Ultrasound Versus Standard Low-resolution Transrectal Ultrasound for the Identification of Clinically Significant Prostate Cancer
ClinicalTrials.gov study NCT02079025. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Clinical Trial of the Treatment of Acute Sinusitis With Standard-dose Versus High-dose Amoxicillin/Clavulanate
ClinicalTrials.gov study NCT02340000. IPD Sharing: NO. Countries: 1. Publications: 2.
A 4-arm Clinical Trial of Fuse®, EndoCuff™, EndoRings™ and Standard Colonoscopy
ClinicalTrials.gov study NCT02345889. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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