Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
19
datasets available to search
ShareScore release 0.9.0
Dataset results
19 results for “clinical corpus”
MiRoR11 - P2 - Annotated corpus for semantic similarity of clinical trial outcomes
<p>Outcome similarity corpus</p> <p>This dataset contains annotations of semantic similarity for pairs of primary and reported outcomes.<br> Tab-separated format is used. The files contain the following columns:<br> filename, sentence pair ID, sentence pair text, primary outcome, primary outcome start position, primary outcome end position, reported outcome, reported outcome start position, reported outcome end position, label</p> <p>The folder out_relations_split contains the dataset splits for 10-fold cross-validation.</p>
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>
CT-EBM-SP - Corpus of Clinical Trials for Evidence-Based-Medicine in Spanish (version 2)
<p>A collection of <strong>1200 texts</strong> (292173 tokens) about<strong> clinical trials studies</strong> and <strong>clinical trials announcements</strong> in <strong>Spanish</strong>:</p> <p>- 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO).<br>- 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos.</p> <p>Texts were annotated with the following entities types:</p> <p>- <strong>Semantic groups from the Unified Medical Language System</strong>: <br> • ANAT: anatomy<br> • CHEM: pharmacological and chemical substances<br> • DEVI: medical devices<br> • DISO: pathologic conditions <br> • LIVB: living beings, included the human being<br> • PHYS: physiological processes<br> • PROC: lab tests, diagnostic or therapeutic procedures<br>- <strong>Medical drug information</strong>:<br> • Contraindicated: a contraindicated drug or treatment<br> • Dose: dose or strength<br> • Form: dosage form<br> • Route: administration route or mode<br>- <strong>Temporal expressions</strong> <br> • Age<br> • Date<br> • Duration<br> • Frequency<br> • Time<br>- <strong>Miscellaneous medical entities</strong>: <br> • Concept: abstract concepts, statistical tests or measurement scales<br> • Food: foods or drinks<br> • Observation: medical observations or clinical findings<br> • Quantifier_or_Qualifier: quantifier or qualifier adjective<br> • Result_or_Value: result or value of a measurement, laboratory analysis or procedure<br>- <strong>Negation/Speculation</strong>: <br> • Neg_cue: negation cue<br> • Negated: negated event<br> • Spec_cue: speculation cue<br> • Speculated: speculated or uncertain event<br>- <strong>Attributes</strong>: <br> • Temporality:<br> ◦ History_of: past event<br> ◦ Future: future event<br> • Experiencer:<br> ◦ Patient: patient or participant on a clinical trial<br> ◦ Family_member<br> ◦ Other: other person different from the patient or the family member</p> <p>86 389 entities and 16 590 attributes were annotated. 10% of the corpus was doubly annotated, and high inter-annotator agreement (IAA) values were achieved: F1-score = 0.84% for entities; and F1-score = 0.88% for attributes (both in strict match). </p> <p>The dataset includes the <strong>texts and annotations used for the human evaluation</strong> of the medical named entity tool:</p> <p>- 100 clinical trial announcements from EudraCT not used for system development: we provide files of the version revised by medical professionals (Reference folder)<br>- 100 clinical cases with Creative Commons license: we provide files with the files revised by medical professionals (Reference folder). These data come from:</p> <p> • Urgencias Bidasoa (https://urgenciasbidasoa.wordpress.com/casos-clinicos-3/)<br> • Hipocampo.org (https://www.hipocampo.org/)<br> • Cases published by Sociedad Andaluza de Medicina Familiar y Comunitaria (SAMFyC): we are greatly thankful for giving us permission to use these cases and we acknowledge that the copyright belongs to the authors' contents. Clinical cases were extracted from books published from 2016 to 2022 (https://www.samfyc.es/tipos-publicacion/publicaciones/).<br> <br>If you use these data, please, acknowledge the copyright and intellectual property rights to the authors' contents.</p> <p>The dataset is freely distributed for research and educational purposes under a Creative Commons Non-Commercial Attribution (CC-BY-NC-A) License.</p> <p>If you use the CT-EBM-SP vs. 2 dataset, please, cite as follows:</p> <p>Campillos-Llanos, L., A. Valverde-Mateos & A. Capllonch-Carrion (2024) Hybrid natural language processing tool for semantic annotation of medical texts in Spanish. BMC Bioinformatics. BioMed Central.</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>
An annotated corpus of clinical trial publications supporting schema-based relational information extraction
<p>Repository of an annotated corpus of clinical trial abstracts supporting schema-based relational information extraction and the code for the inter-annotation agreement calculation and the baseline information extraction method.</p>
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus
<p>We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scores $\simeq 0.85$ on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction.</p>
DisTEMIST corpus: detection and normalization of disease mentions in spanish clinical cases
<p><strong>DisTEMIST corpus: training set + MULTILINGUAL RESOURCES + CROSSMAPPINGS + test set + background set</strong></p><p> </p><p><strong>Please cite if you use this dataset:</strong></p><p>Miranda-Escalada, A., Gascó, L., Lima-López, S., Farré-Maduell, E., Estrada, D., Nentidis, A., Krithara, A., Katsimpras, G., Paliouras, G., & Krallinger, M. (2022). Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources. <i>Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings</i></p><p>@article{miranda2022overview, title={Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources}, author={Miranda-Escalada, Antonio and Gascó, Luis and Lima-López, Salvador and Farré-Maduell, Eulàlia and Estrada, Darryl and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras, Georgios and Paliouras, Georgios and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2022} }</p><p> </p><p><strong>Introduction</strong></p><p>The DisTEMIST corpus is a collection of 1000 clinical cases with <strong>disease mention annotations manually mapped to</strong> <strong>Snomed-CT concepts</strong>. All documents are released in the context of the BioASQ DisTEMIST track for CLEF 2022. For more information about the track and its schedule, please <a href="https://temu.bsc.es/distemist/">visit the website</a>.</p><p> </p><p><strong>File structure:</strong></p><p>The DisTEMIST corpus has been randomly divided into a training set, containing 750 clinical cases, and a test set (584 in the case of subtrack2), consisting of 250 additional cases. Participants must train their systems using the train set and submit predictions for the test set, on which they will be evaluated. The file structure of the corpus is as follows:</p><ul><li><strong>train_set:</strong><ul><li><strong>text_files</strong>: Folder with plain text files of the clinical cases</li><li><strong>subtrack1_entities</strong>: It contains annotations in a tab-separated file (TSV) with the following columns:<ul><li><i>filename</i>: document name</li><li><i>mark</i>: identifier mention id</li><li><i>label</i>: mentions type (ENFERMEDAD)</li><li><i>off0</i>: starting position of the mention in the document</li><li><i>off1</i>: ending position of the mention in the document</li><li><i>span</i>: text span</li></ul></li><li><strong>subtrack2_linking: </strong>It contains annotations in a tab-separated file (TSV) with the following columns:<ul><li><i>filename</i>: document name</li><li><i>mark</i>: identifier mention id</li><li><i>label</i>: mentions type (ENFERMEDAD)</li><li><i>off0</i>: starting position of the mention in the document</li><li><i>off1</i>: ending position of the mention in the document</li><li><i>span</i>: text span</li><li><i>codes</i>: List of Snomed-CT concept codes linked to the mention. If there is more than one code associated with a mention, they will be concatenated by the symbol "+".</li><li><i>semantic relation</i>: the relationship between the assigned code and the mention. It can be EXACT, when the code corresponds exactly with the mention, or NARROW, when the mention corresponds to a narrower concept than the Snomed-CT code. For instance, the concept "Chorioretinal lacunae" does not exist in Snomed-CT. Then, it is normalized to the Snomed-CT ID 302893000 ("Chorioretinal disorder").</li><li>(Note: the training for entity linking were released in two parts, you must join them)</li></ul></li></ul></li></ul><p> </p><ul><li><strong>test_annotated:</strong><ul><li><strong>text_files</strong>: Folder with plain text files of the clinical cases</li><li><strong>brat</strong>: Folder with text files and their annotations in brat's .ann format</li><li><strong>subtrack1_entities: </strong>It contains annotations in a tab-separated file (TSV) with the same columns as the train set</li><li><strong>subtrack2_linking: </strong>It contains annotations in a tab-separated file (TSV) with the same columns as the train set</li></ul></li></ul><p> </p><ul><li><strong>test_background_unannotated/text_files: </strong>3000 clinical cases (test + background). In the the original task, participants had to make predictions for these 3000 files and they were evaluated on a subset of them.</li></ul><p> </p><ul><li><strong>multilingual-resources</strong>: we have generated the annotated training and validation sets in <strong>6 languages</strong>: <i><strong>English</strong></i>, <i><strong>Portuguese</strong></i>, <i><strong>Catalan</strong></i>, <i><strong>Italian</strong></i>, <i><strong>French</strong></i> and <i><strong>Romanian</strong></i>. The process was:<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 an annotation transfer technology.</li><li>The text files are stored in the multilingual_resources/training-text-files subfolder.</li><li>The annotated TSV files are stored in the multilingual_resources/lang subfolders.</li><li>If you want to visualize the multilingual resources, check out this Brat server: <a href="https://temu.bsc.es/mDistemist/#/translations/">https://temu.bsc.es/mDistemist/#/translations/</a><br>For instance, you can see the parallel annotations in <a href="http://temu.bsc.es/mDistemist/diff.xhtml#/translations/fr/train/es-S0004-06142008000100011-1?diff=/translations/en/train/">English vs in French</a>, or in <a href="https://temu.bsc.es/mDistemist/diff.xhtml#/translations/cat/train/S0004-06142005000500011-1?diff=/gold-standard/train/">Spanish (the gold standard) vs in Catalan</a>.</li></ol></li></ul><p> </p><ul><li><strong>cross-mappings. </strong>We include the same entities as in DISTEMIST-linking but mapped to Snomed-CT, <a href="https://www.ncbi.nlm.nih.gov/mesh/"><strong>MeSH</strong></a>, <a href="https://icd.who.int/browse10/2019/en#/"><strong>ICD-10</strong></a>, <a href="https://hpo.jax.org/"><strong>HPO</strong></a>, and <a href="https://www.omim.org/"><strong>OMIM</strong></a>. The original mappings are manual and to Snomed-CT. The mapping to the other terminologies was done through the UMLS Metathesaurus.</li></ul><p> </p><p><strong>Resources</strong> </p><ul><li><a href="https://temu.bsc.es/distemist/">Task Web</a></li><li><a href="https://github.com/TeMU-BSC/distemist_evaluation_library">Evaluation Library</a></li><li><a href="https://doi.org/10.5281/zenodo.6458114"><strong>DISTEMIST gazetteer</strong></a></li><li><a href="https://doi.org/10.5281/zenodo.6458078"><strong>DISTEMIST guidelines</strong></a></li><li><strong>Citation: </strong>Miranda-Escalada, A., Gascó, L., Lima-López, S., Farré-Maduell, E., Estrada, D., Nentidis, A., Krithara, A., Katsimpras, G., Paliouras, G., & Krallinger, M. (2022). Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources. <i>Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings</i></li><li><a href="https://ceur-ws.org/Vol-3180/paper-11.pdf"><strong>Overview paper</strong></a></li><li><a href="https://ceur-ws.org/Vol-3180/"><strong>CLEF/BioASQ Workshop proceedings</strong></a></li><li><a href="https://www.youtube.com/watch?v=KvvhogUMjl8&list=PL5uSCzf1azhC_Sg9leXavmcA4nJ4HQa_n"><strong>Youtube videos (overview & teams)</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/overview-of-distemist-at-bioasq-automatic-detection-and-normalization-of-diseases-from-clinical-texts-results-methods-evaluation-and-multilingual-resources-at-bioasq-workshop-of-clef-conference"><strong>DISTEMIST overview talk slides</strong></a></li></ul><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>Additional resources and corpora</strong></p><p>If you are interested in DisTEMIST, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/8413866">SymTEMIST</a> (Corpus of symptoms, signs and findings 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/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 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><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>
CT-EBM-SP - Corpus of Clinical Trials for Evidence-Based-Medicine in Spanish
<p>A collection of 1200 texts (292 173 tokens) about clinical trials studies and clinical trials announcements in Spanish:</p> <p>- 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO).<br> - 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos.</p> <p>Texts were annotated with entities from the Unified Medical Language System semantic groups: anatomy (ANAT), pharmacological and chemical substances (CHEM), pathologies (DISO), and lab tests, diagnostic or therapeutic procedures (PROC). 46 699 entities were annotated (13.98% are nested entities). 10% of the corpus was doubly annotated, and inter-annotator agreement (IAA) achieved a mean F-measure of 85.65% (±4.79, strict match) and a mean F-measure of 93.94% (±3.31, relaxed match). </p> <p>The corpus is freely distributed for research and educational purposes under a Creative Commons Non-Commercial Attribution (CC-BY-NC-A) License.</p>
Biomedical Entities and Relations on Spanish Clinical Case Corpus: BERSCCC
<p>This first version of a spanish corpus contains 200 clinical reports annotated with biomedical entities and semantic relations. <br> These reports belong to the Spanish Clinical Case Corpus (SPACCC) (https://doi.org/10.5281/zenodo.2560316)<br> and each of them has been annotated by three persons that work in the medicine, biomolecular or pharmaceutic area.</p> <p>The annotators had to identify the following thirteen types of entities in the spanish lenguage: Enfermedad/Síndrome, Gen, Parte del cuerpo/Órgano, Glúcido, Procedimiento de Diagnóstico, Proteína, Procedimiento Terapeútico, Síntoma/Signo, Sustancia Farmacológica, Lípido, Organismo, Químico Orgánico and Abreviatura/Sigla/Alias.<br> And the next eight semantic relations: Analiza, Altera, Causa, Diagnostica, Manifestación de, Produce, Trata and Refiere a. </p> <p>Finally there were identified 6,636 biomedical entities (37,081 mentions) and 4,864 semantic relations (7,622 mentions).</p> <p>These resources are freely distributed under a Creative Commons Attribution 4.0 International License.<br> The scripts used to create this corpus can be found at: https://github.com/drugs4covid/bio-corpora</p> <p>Author: Lucía Sánchez González, Ontology Engineering Group, Universidad Politécnica de Madrid.</p> <p>Supervisors: </p> <p>- Carlos Badenes Olmedo, Ontology Engineering Group, Universidad Politécnica de Madrid.</p> <p>- María Poveda Villalón, Ontology Engineering Group, Universidad Politécnica de Madrid.</p> <p>Project Member: Óscar Corcho García, Ontology Engineering Group, Universidad Politécnica de Madrid.</p> <p> Contact:</p> <p>Lucía Sánchez González at lu.sanchez@alumnos.upm.es or lusangonz99@gmail.com</p> <p>Acknowledgments to Project DRUGS4COVID++: Servicios de Inteligencia Artificial para la<br> creación de un grafo de conocimientos sobre fármacos usados en el control clínico de la<br> enfermedad, a partir de la explotación de grandes corpus de documentación científica sobre<br> SARS-COV-2 y COVID-19-AYUDAS FUNDACIÓN BBVA A EQUIPOS DE<br> INVESTIGACIÓN CIENTÍFICA SARS-CoV-2 y COVID-19.</p> <p> </p>
MultiCardioNER Corpus: Multilingual Adaptation of Clinical NER Systems to the Cardiology Domain
<h1><strong>MultiCardioNER</strong></h1> <p><strong>MultiCardioNER</strong> is a shared task about the adaptation of clinical NER systems to the cardiology domain. It uses a combination of two existing datasets (DisTEMIST for diseases and the newly-released DrugTEMIST for medications), as well as a new, smaller dataset of cardiology clinical cases annotated using the same guidelines.</p> <p>Participants are provided DisTEMIST and DrugTEMIST as training data to use as they see fit (1,000 documents, with the original partitions splitting them into 750 for training and 250 for testing). The cardiology clinical cases (cardioccc) are meant to be used as a development or validation set (258 documents), although participants are encourage to experiment with the documents and annotations as they see fit. The evaluation is done using a different collection of cardiology clinical cases (250).</p> <p>MultiCardioNER proposes two tracks:</p> <p>- Track 1: Spanish adaptation of disease recognition systems to the cardiology domain.<br>- Track 2: Multilingual (Spanish, English and Italian) adaptation of medication recognition systems to the cardiology domain.</p> <p>Please read the README file attached for more information on folder structure and file format.</p> <p><strong>MultiCardioNER</strong> was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioASQ 2024. For more information on the corpus, annotation scheme and task in general, please visit: <a href="https://temu.bsc.es/multicardioner" target="_blank" rel="noopener">https://temu.bsc.es/multicardioner</a>. This task is promoted by Spanish and European projects such as DataTools4Heart, AI4HF, BARITONE and AI4ProfHealth.</p> <p><strong>UPDATE MAY 28th 2024: </strong>The test set annotations are now out! We've also included the original background set files, as well as a file with the mappings from the masked filenames used during the evaluation phase to the original filenames. Please check the README for more information.</p> <h2><strong>Resources</strong></h2> <ul> <li><a href="https://temu.bsc.es/multicardioner" target="_blank" rel="noopener">MultiCardioNER website</a></li> <li><a href="http://bioasq.org/" target="_blank" rel="noopener">BioASQ website</a></li> <li><a href="../doi/10.5281/zenodo.6458078" target="_blank" rel="noopener">DisTEMIST Guidelines</a></li> <li><a href="../doi/10.5281/zenodo.11065432" target="_blank" rel="noopener">DrugTEMIST Guidelines</a></li> </ul> <h2><strong>License</strong></h2> <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> <h2><strong>Contact</strong></h2> <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> <h2><strong>Additional resources and corpora</strong></h2> <p>If you are interested in MultiCardioNER, you might want to check out these corpora and resources:</p> <ul> <li><a href="../records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT)</li> <li><a href="../records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT)</li> <li><a href="../records/10635215">SympTEMIST</a> (Corpus of clinical findings and normalization to SNOMED CT)</li> <li><a href="../records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization)</li> <li><a href="../records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization)</li> <li><a href="../records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization)</li> <li><a href="../records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI))</li> <li><a href="../records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization)</li> <li><a href="../records/3837305">CodiESP</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version)</li> <li><a href="../records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy)</li> <li><a href="../records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags)</li> <li><a href="../records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries))</li> <li><a href="../records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags)</li> <li><a href="../records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts)</li> </ul>
ClinSpEn Corpus: Parallel English-Spanish COVID-19 Clinical Cases, Terminology and Ontology Concepts
<p><strong>ClinSpEn Parallel Corpus Collection</strong></p> <p>This repository contains the <strong>complete</strong> <strong>ClinSpEn corpus collection</strong>, which was used for the <strong><a href="https://temu.bsc.es/clinspen">ClinSpEn shared task</a></strong> at Biomedical WMT 2022.</p> <p>ClinSpEn is a collection of <strong>Gold Standard EN-ES parallel corpora of different types of clinical data</strong>: case reports, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content. It includes development and test data translated by professional medical translators that can be used <strong>to train and benchmark clinical EN-ES machine translation systems</strong>. Additionally, monolingual background data is provided so that the systems' performance can be analyzed in unseen data.</p> <p>If you use this dataset, please cite:</p> <blockquote> <pre><code>inproceedings{biowmt22, title={Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports}, author={Neves, Mariana and Yepes, Antonio Jimeno and Siu, Amy and Roller, Roland and Thomas, Philippe and Navarro, Maika Vicente and Yeganova, Lana and Wiemann, Dina and Di Nunzio, Giorgio Maria and Vezzani, Federica and others}, booktitle={WMT22-Seventh Conference on Machine Translation}, pages={694--723}, year={2022} } </code></pre> </blockquote> <p><strong>Data Description</strong></p> <p>ClinSpEn proposes three different sub-tracks, each based on a different type of clinical data:</p> <p><em>1. Clinical Cases:</em></p> <p>Parallel EN-ES COVID-19 clinical case reports. The direction of this sub-track is EN>ES.</p> <p>The dataset’s case reports were carefully selected to cover a wide range of aspects related to the disease: different types of patients (children, adults, elderly and pregnant people, babies), different comorbidities (cancer, mental health issues, immunosuppressed patients) and symptomatology (mild and severe presentations, dermatologic, immunologic and psychiatric manifestations, thrombosis, ...). The reports were translated from English to Spanish by a professional medical translator on a first step and revised by a clinical expert on a second step.</p> <p>The sample (dev) set and test set are made up of parallel txt files (50 and 152 documents each, respectively), with the Spanish version having a “.es” extension and the English files having a “.en” extension. Each report has been parallelized so that every sentence’s line number corresponds to the same sentence’s line number in both languages.</p> <p>The background data (9,804 files) is made up of a TSV file with four columns: filename, document number, line number and English line. The clinical cases themselves include COVID-19 case reports as well as diverse content extracted from PubMed.</p> <p>If you need to map the entries in the join test + background document provided in earlier versions, you may use the "clinspen_clinicalcases_test-set_filename_mapping.tsv" file.</p> <p><em>2. Clinical Terminology:</em></p> <p>Parallel EN-ES clinical terms extracted from medical literature and clinical records, with particular focus on diseases, symptoms, findings, procedures and professions and translated and revised by professional medical translators. The direction of this sub-track is ES>EN.</p> <p>The sample (dev) set contains 7,000 terms as a tab-separated file (TSV), with the first column corresponding to English terms and the second column to Spanish terms.</p> <p>The test data (12,128 terms) is made up of a TSV file with three columns: term number, English term and Spanish term.</p> <p>The background data (201,890 terms) is made up of a TSV file with two columns: term number and Spanish term.</p> <p>The term number columns can be used to map the entries in the join test + background document provided in earlier versions.</p> <p><em>3. Ontology Concepts:</em></p> <p>Parallel EN-ES concepts extracted from various open biomedical ontologies and taxonomies and then manually translated by a professional medical translator. The direction of this sub-track is EN>ES.</p> <p>The sample (dev) data includes 400 concepts. The terms are presented as tab-separated file (TSV), with the first column corresponding to English terms and the second column to Spanish terms. The third column includes the term’s origin ontology and its correspondent ID (separated by an underscore), while the fourth one includes a link to the concept in OBO Library.</p> <p>The test data (1,789 concepts) is made up of a TSV file with five columns: term number, English term, Spanish term, ontology id and OBO library URL.</p> <p>The background data (299,408 concepts) is made up of a TSV file with four columns: term number, English term, ontology id and OBO library URL.</p> <p>The term number columns can be used to map the entries in the join test + background document provided in earlier versions.</p> <p> </p> <p><strong>Related Links:</strong></p> <ul> <li> <p>ClinSpEn website with more information: <a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> </li> <li> <p>WMT website: <a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> </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 question or suggestion, please contact us at the following addresses:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br> - Darryl Estrada (<darrylestrada97 [at] gmail [dot] com>)<br> - Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p> </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>
Cantemist corpus: gold standard of oncology clinical cases annotated with CIE-O 3 terminology
<p><strong>Intro:</strong></p> <p>Cantemist shared task dataset (divided in train, dev1, dev2 and test). In addition, we include here the Cantemist background set.</p> <p>It contains the train, development and test sets of the three subtasks: cantemist-ner, cantemist-norm and cantemist-coding with Gold Standard annotations.</p> <p>In addition, it contains the documents of the background set, without annotations.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</p> <pre><code>@inproceedings{miranda2020named, title={Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results}, author={Miranda-Escalada, A and Farr{\'e}, E and Krallinger, M}, booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings}, year={2020} }</code></pre> <p> </p> <p><strong>Format:</strong></p> <p>For subtasks cantemist-norm and cantemist-ner, annotations are distributed in Brat format. See <a href="https://brat.nlplab.org/standoff.html">Brat webpage</a> for more information</p> <p>For subtask cantemist-coding, codes are grouped in a TSV file with the following columns (this follows the format used in <a href="https://temu.bsc.es/codiesp/">CodiEsp shared task</a>): </p> <blockquote> <p>filename code</p> </blockquote> <p> </p> <p><strong>Shared task goal:</strong></p> <p>In the three subtasks, the goal will be to predict the annotations (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><strong><a href="https://temu.bsc.es/cantemist/">Web</a></strong></li> <li><strong><a href="http://ceur-ws.org/Vol-2664/cantemist_overview.pdf">Citation</a>: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><strong><a href="https://doi.org/10.5281/zenodo.4010899">Silver Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>For further information, please visit <a href="https://temu.bsc.es/cantemist/">https://temu.bsc.es/cantemist/</a> or email us at encargo-pln-life@bsc.es</p>
SympTEMIST Corpus: Gold Standard annotations for clinical symptoms, signs and findings information extraction
<p><strong>SympTEMIST</strong> stands for Symptoms TExt MIning Shared Task. It is a shared task and set of resources focused on the <strong>detection of mentions, normalization and indexing of symptoms, signs and findings in medical documents</strong> in <strong>Spanish</strong>. SympTEMIST is complementary to the DisTEMIST (https://temu.bsc.es/distemist) and MedProcNER/ProcTEMIST (https://temu.bsc.es/medprocner) corpora as they all use the same document collection.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>"Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>."</p> <p> </p> <p>This repository includes the:</p> <ul> <li><strong>Train </strong>and<strong> Test Set </strong>for the three subtasks</li> <li><strong>SYMPTEMIST gazetteer </strong>of <strong>SNOMED symptoms, signs & findings </strong></li> <li><strong>Multilingual </strong>Silver Standard<strong> </strong>in <strong>9 languages:</strong> <ul> <li><em><strong>English</strong></em></li> <li><em><strong>Portuguese</strong></em></li> <li><em><strong>French</strong></em></li> <li><em><strong>Italian</strong></em></li> <li><em><strong>Romanian</strong></em></li> <li><em><strong>Catalan</strong></em></li> <li><em><strong>Swedish</strong></em></li> <li><em><strong>Dutch</strong></em></li> <li><em><strong>Czech</strong></em></li> </ul> </li> <li><strong>Background set</strong> of over 15,000 clinical cases.</li> <li><strong>Spanish</strong> Silver Standard (predictions by participants over the background set)</li> </ul> <p> </p> <p>Please read the README file attached for more information on folder structure and file format.</p> <p> </p> <p>SympTEMIST was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioCREATIVE 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/symptemist.</p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><a href="https://temu.bsc.es/symptemist/"><strong>Task web</strong></a></li> <li><a href="https://biocreative.bioinformatics.udel.edu/"><strong>BioCreative web</strong></a></li> <li><strong>Citation: </strong>Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>.</li> <li><a href="../records/8246440"><strong>Annotation guidelines</strong></a></li> <li><a href="../records/10103191"><strong>BioCreative/AMIA workshop proceedings</strong></a></li> <li><a href="../doi/10.5281/zenodo.10104546"><strong>Overview paper</strong></a></li> <li><a href="https://www.youtube.com/playlist?list=PLyLDDulunoofv79Pci0-y2rGP5GOBoZhp"><strong>Youtube videos (overview & teams)</strong></a></li> <li><a href="https://www.slideshare.net/MartinKrallinger/symptemist-shared-task-on-symptoms-signs-and-findings-detection-and-normalization-task-overview-talk-at-biocreative-viii-workshop-amia-2023"><strong>SympTEMIST overview talk slides at BioCreative/AMIA workshop</strong></a></li> </ul> <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><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> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested in SympTEMIST, you might want to check out these corpora and resources:</p> <ul> <li><a href="../records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection)</li> <li><a href="../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="../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="../records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li> <li><a href="../records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li> <li><a href="../records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li> <li><a href="../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="../records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li> <li><a href="../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="../records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li> <li><a href="../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> </p>
Gene alterations, clinical informations and survival data of gliomas with/without corpus callosum involvement
<p>The raw dataset of this study contained the gene alterations, clinical informations and survival data of glioma patients. Gene alterations data were obtained by Next-generation DNA sequencing of glioma samples of patients. Clinical informations were obtained from Electronic medical records and follow-up. The survival data were obtained by follow-up of patients. In order to explore gene alterations and their correlation with the survival of glioblastoma (GBM) of corpus callosum (CC) involvement (ccGBM), this dataset was analyzed in detail. A total of<b> </b>30 ccGBM and 88 non-ccGBM were finally included. The ccGBM had higher incidence of <i>PDGFRA </i>(33.3% versus 9.1%, P=0.004) alterations than non-ccGBM. <i>PDGFRA </i>amplification (<i>PDGFRAamp</i>, 33.3% versus 9.1%, P=0.004) and missense mutation (<i>PDGFRAmut</i>, 20.0% versus 3.4%, P=0.011) both had higher incidence in ccGBM than in non-ccGBM. <i>PDGFRA </i>alteration was significantly associated with the occurrence of ccGBM (OR=4.91 [95%CI: 1.55-15.52], P=0.007). The ccGBM of <i>PDGFRAamp </i>achieved shorter median PFS (8.6 versus 13.5 months, P=0.025) and OS (12.4 versus 17.9 months, P=0.022) than non-ccGBM of <i>PDGFRAnon-amp</i>. The ccGBM of <i>PDGFRAamp</i> combined with <i>PDGFRAmut</i> (<i>PDGFRAamp-mut</i>)<i> </i>had shorter median PFS (7.6 versus 8.9 months, P=0.022) and OS (9.6 versus 17.8 months, P=0.006) than non-ccGBM of <i>PDGFRA</i> wild type and non-amplification (<i>PDGFRA-w, non-amp</i>). Compared to ccGBM of <i>PDGFRA-w, non-amp</i>, the ccGBM of <i>PDGFRAamp</i> and <i>PDGFRAamp-mut </i>both had shorter median PFS and OS (P<0.05). Hazard ratios (HRs) of <i>PDGFRAamp</i> for PFS and OS of ccGBM were 3.08 (95%CI: 1.02-9.35, P=0.047) and 5.07 (95%CI: 1.52-16.89, P=0.008) respectively, HRs of <i>PDGFRAamp-mut </i>for PFS and OS were 13.16 (95%CI: 3.19-54.40, P<0.001) and 16.36 (95%CI: 2.66-100.70, P=0.003) respectively. The <i>PDGFRA </i>alterations is significantly associated with the occurrence and poor prognosis of ccGBM.</p>
Gene alterations, clinical informations and survival data of gliomas with/without corpus callosum involvement
Open the record for dataset details and reuse information.
GraSCCo_PII_V2 - Graz Synthetic Clinical text Corpus with PII Annotations
<p><strong>GraSCCo_PII_V2 - Graz Synthetic Clinical text Corpus with Personally Identifiable Informations</strong></p> <p>Additional source of:</p> <ul> <li>Lohr C, Faller J, Riedel A, Nguyen HM, Wolfien M, Hofenbitzer J, Modersohn L, Romberg J, Prasser F, Omeirat J, Wen Y, Galusch O, Hahn U, Seiferling M, Dieterich C, Klügl P, Matthies F, Kind J, Boeker M, Löffler M, Meineke F. <strong>GeMTeX's De-Identification in Action: Lessons Learned & Devil's Details</strong>. Stud Health Technol Inform. 2025 Sep 3;331:274-282. doi: 10.3233/SHTI251406. PMID: 40899551. (<a href="https://pubmed.ncbi.nlm.nih.gov/40899551/">https://pubmed.ncbi.nlm.nih.gov/40899551/</a>)</li> </ul> <p>GraSCCo is a collection of artificially generated semi-structured and unstructured German-language clinical summaries. These summaries are formulated as letters from the hospital to the patient's GP after in-patient or out-patient care. Details:</p> <ul> <li>Stefan Schulz. (2022). GraSCCo (Version v1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6539131" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.6539131</a></li> <li>Modersohn L, Schulz S, Lohr C, Hahn U. GRASCCO - The First Publicly Shareable, Multiply-Alienated German Clinical Text Corpus. <em>Stud Health Technol Inform</em>. 2022;296:66-72. doi:10.3233/SHTI220805</li> </ul> <p><strong>First Version - GraSSCo</strong> <strong>with</strong> PHI (now named as <strong>PII</strong>) annotations as external source of</p> <ul> <li><strong>Lohr C, Matthies F, Faller J, et al. De-Identifying GRASCCO - A Pilot Study for the De-Identification of the German Medical Text Project (GeMTeX) Corpus. <em>Stud Health Technol Inform</em>. 2024;317:171-179. doi:10.3233/SHTI240853 (<a href="https://pubmed.ncbi.nlm.nih.gov/39234720/">https://pubmed.ncbi.nlm.nih.gov/39234720/</a>)<br></strong></li> </ul> <p>This repository contains the annotations in XMI and JSON exports created with the INCEpTION annotation platform (<a href="https://inception-project.github.io/">https://inception-project.github.io/</a>), also the annotation guideline document, TypeSystem.xml and layer.json (needed for import in INCEpTION), see also <a href="https://github.com/dkpro/dkpro-cassis">https://github.com/dkpro/dkpro-cassis</a>.</p>
De-identified UNMH EEG Corpus Database Creation With Fully De-identified Clinical Information
ClinicalTrials.gov study NCT05140265. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.