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131 results for “clinical cases”
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>
MultiCaRe: An open-source clinical case dataset for medical image classification and multimodal AI applications
<p>The dataset contains multi-modal data from over 70,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 130,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv.</p> <p>More than 90,000 patients and 280,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file.</p> <p>Refer to the examples showcased in this <a href="https://github.com/mauro-nievoff/MultiCaRe_Dataset">GitHub repository</a> to understand how to optimize the use of this dataset.<br><br>The license of the dataset as a whole is CC BY-NC-SA. However, its individual contents may have less restrictive license types (CC BY, CC BY-NC, CC0). For instance, regarding image filess, 66K of them are CC BY, 32K are CC BY-NC-SA, 32K are CC BY-NC, and 20 of them are CC0.</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>
ClinSpEn-CC (Clinical Cases) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Clinical Cases sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with three columns: document number, line number and English line. The direction of this sub-track is EN>ES. The clinical cases themselves include COVID-19 case reports as well as diverse content extracted from PubMed.</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </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>
Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University)
<p>This video is the seventh talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University)</p> <p>Bio: Bryant Lin, MD, MEng is a primary care physician, educator and researcher. The cornerstone of Dr. Lin's work is keeping medicine focused on humans - patients, providers, families and trainees - and not lost in technology and algorithms. His research and educational interests span (1) Developing and testing novel medical technologies, (2) Improving the health of Asian populations with Precision and Population Health, and (3) Increasing expression and interconnections in the Health Community with the Humanities and Arts. After receiving his undergraduate and master's degrees in Electrical Engineering and Computer Science from MIT, he completed his MD and internal Medicine training at Tufts University School of Medicine and Tufts Medical Center. He came to Stanford to serve as a Research Fellow in Cardiac Electrophysiology and Biodesign Fellow where he learned to identify unmet human-centered needs. Since completing his post-graduate training, he stayed at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine where he has invented and researched new medical technologies addressing unmet human-centered needs and started the Consultative Medicine Clinic evaluating patients with medical mysteries. He serves as the Training Director for the Joe and Linda Chlapaty DECIDE Center which has created a novel shared decision making tool for atrial fibrillation anticoagulation and is an investigator in several active clinical trials. Three years ago, he co-founded and currently co-directs, with Dr. Latha Palaniappan, the Center for Asian Health Research and Education (CARE) which aims to improve the health of Asians everywhere. Most recently, he has worked closely with the Medicine and the Muse leadership to help start the Stuck@Home concert series, the Stanford SoundWalk and the COVID Remembrance project. Dr. Lin has an active interest in storytelling and film-making. He co-directs an undergraduate seminar, MED 53Q “Storytelling in Medicine”, with Dr. Lauren Edwards and is working with a group of students on a documentary on end-of-life care at a JapaneseAmerican Senior Home in the Bay Area.</p> <p>Kevin Chang MD, MS, is a primary care physician. His focus in on patient care, population health and quality improvement, and medical education. He received his undergraduate degree and master's degree in biomedical engineering from Duke University and Stanford University respectively, and completed his MD at New York University, followed by his medical training at Stanford University. He has since stayed on at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine, where he also serves as the co-director of the resident physician Internal Medicine clinic.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ozk1iJYC1yk</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>
Data from: The clinical, histological, and genotypic spectrum of SEPN1-related myopathy: a case series
<p><b><span>Objective: </span></b><span>To clarify the prevalence, long-term natural history and severity determinants of SEPN1-related myopathy (SEPN1-RM), we analyzed a large international case series. </span></p> <p><b>Methods: </b>Retrospective clinical, histological and genetic analysis of 132 pediatric and adult patients (2-58 years) followed-up for several decades.</p> <p><b><span>Results: </span></b><span>The clinical phenotype was marked by severe axial muscle weakness, spinal rigidity and scoliosis (86.1%, from 8.9±4 years), with relatively-preserved limb strength and previously-unreported ophthalmoparesia in severe cases. All patients developed respiratory failure (from 10.1±6 years), 81.7% requiring ventilation while ambulant. Histopathologically, 79 muscle biopsies showed large variability, partly determined by site of biopsy and age. Multi-minicores were the most common lesion (59.5%), often associated with mild dystrophic features and occasionally with eosinophilic inclusions. Identification of 65 SEPN1 mutations, including 32 novel ones and the first pathogenic CNV, unveiled exon 1 as the main mutational hotspot and revealed the first genotype-phenotype correlations, bi-allelic null mutations being significantly associated with disease severity (<i>p</i>=0.017). SEPN1-RM was more severe and progressive than previously thought, leading to loss of ambulation in 10% cases, systematic functional decline from the end of the third decade and reduced lifespan even in mild cases. The main prognosis determinants were scoliosis/respiratory management, <i>SEPN1</i> mutations and body mass abnormalities, which correlated with disease severity. Finally, we propose a set of severity criteria, provide quantitative data for outcome identification and establish a need for age stratification.</span></p> <p><b><span>Conclusion</span></b><span>: Our results inform clinical practice, improving diagnosis and management, and represent a major breakthrough for clinical trial readiness in this not-so-rare disease.</span></p>
AutoImplant 2021 Task 2 - Clinical Cases Requiring Cranioplasty
<p>Clinical cases of defective skulls from the University of Nebraska Medical Center prior to correction via cranioplasty. Images are binary images that were thresholded at 150 Hounsfield units from high resolution CT scans. Facial bone features have been removed.</p>
Supplementary material (Sanger sequencing photo) of the case of THRB mosaicism presented in Clinically Symptomatic Resistance to Thyroid Hormone β Syndrome Because of THRB Gene Mosaicism of Donnars et al. (https://doi.org/10.1210/clinem/dgac347)
<p>Supplementary material (Sanger photo) of the case of THRB mosaicism presented in "Clinically Symptomatic Resistance to Thyroid Hormone β Syndrome Because of THRB Gene Mosaicism", by Donnars et al. (https://doi.org/10.1210/clinem/dgac347)</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>
"COVID toes": a meta-analysis of case and observational studies on clinical, histopathological and laboratory findings.
<p><strong>Background: </strong>Coronavirus disease (COVID-19) is related to several extrapulmonary disorders; however, little is known about the clinical, laboratory and histopathological characteristics of pernio-like skin lesions associated with COVID-19 infection. <strong>Objective: </strong>To evaluate and summarize the clinical, laboratory and histopathological characteristics of pernio-like lesions reported in the literature. <strong>Methods: </strong>We conducted a search of the PubMed, SciELO and ScienceDirect databases for articles published between 1 January 2020 and 30 November 2020, following the PRISMA recommendations (PROSPERO registration ID: CRD42020225055). The target population was individuals with suspected or laboratory-confirmed COVID-19 with pernio-like lesions. Observational studies, research letters and case/series reports were all eligible for inclusion. Observational studies were evaluated using a random-effects model to calculate the weighted mean prevalence, overall mean and 95% confidence interval. We evaluated case studies using the chi-square test for dichotomous variables and the Mann-Whitney test for continuous variables. <strong>Results:</strong> A total of 187 patients from case reports and 715 patients from 18 observational studies were included. The mean age of patients was 16.6 (14.5–18.8) years. Feet were affected in 91.4% (87.0–94.4%) of patients in observational studies. The proportion of patients with a positive RT-PCR test was less than 15%. Lesion topography and morphology were associated with age. <strong>Conclusion:</strong> Lesions mostly occurred in pediatric patients, and the morphological characteristics tended to differ between pediatric and non-pediatric populations. There is a possible multifactorial component in lesion pathophysiology. The non-positivity of laboratory tests does not exclude an association with COVID-19. Chilblain-like lesions may be a late manifestation of COVID-19.</p>
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>
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>
Effect of AI Chatbot-Assisted Versus Traditional Case-Based Learning on Clinical Reasoning in Occupational Therapy Students: A Study on Parkinson's Disease
ClinicalTrials.gov study NCT07045077. IPD Sharing: NO. Countries: 1. Publications: 1.
BIO4 Clinical Case Study: Cervical Spine
ClinicalTrials.gov study NCT03077204. IPD Sharing: NO. Countries: 1. Publications: 4.
Comparison of Epidemiology and Clinical Outcomes of Influenza A & B Cases in Manitoba, Canada
ClinicalTrials.gov study NCT01683630. IPD Sharing: NO. Countries: 1. Publications: 1.
Implementing virtual workspaces for clinical research at an academic health center: A case report
Open the record for dataset details and reuse information.
Data from: The clinical, histological, and genotypic spectrum of SEPN1-related myopathy: A case series
Open the record for dataset details and reuse information.
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Allen Brain Atlas
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International Brain Laboratory public data
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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.