Find research datasets worth reusing

opencc-by-4.0Jun 2021View details →

Datasets for "A Bibliometric Study of Medical Tourism" paper

<p>This datasets is used during the "<strong>A Bibliometric Study of Medical Tourism</strong>" study.  The data were collected from the SCOPUS database. A title search on SCOPUS based on the search term retrieved 1,535 documents consisting of 969 Articles, 197 book chapters, 102 review papers, 83 conference papers, while the rest were notes, letter, editorials, short surveys, books, and erratum. . The timespan covered ranged from 1<sup>st</sup> of 1952 to 2020.</p>

opencc-by-4.0Jun 2022View details →

Dataset for the paper "The relationship between medical students' empathy, mental health, and burnout: A cross-sectional study" published in Medical Teacher (2022)

<p><strong>Full reference of the paper: </strong></p> <p>Valerie Carrard, Céline Bourquin, Sylvie Berney, Katja Schlegel, Jacques Gaume, Pierre-Alexandre Bart, Martin Preisig, Marianne Schmid Mast & Alexandre Berney (2022): The relationship between medical students’ empathy, mental health, and burnout: A cross-sectional study, Medical Teacher, DOI: <a href="https://doi.org/10.1080/0142159X.2022.2098708">10.1080/0142159X.2022.2098708</a></p>

opencc-by-4.0Jan 2023View details →

Immersive haptic simulation for training nurses in emergency medical procedures - Data collected and statistical analysis

<p>Data collected during the evaluation presented in "Haptic simulation for emergency procedures in nursing training" paper.</p> <table> <caption>HR ALL</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.857</td> <td>29</td> <td>0.008</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-8.089</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>7.567</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.962</td> <td>29</td> <td>0.006</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST MANN</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>1.665</td> <td>14</td> <td>0.118</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-7.104</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>6.498</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-1.461</td> <td>14</td> <td>0.166</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR FIRST VR</caption> <thead> <tr> <th>Measure 1</th> <th> </th> <th>Measure 2</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-</td> <td>Mann post HR</td> <td>2.341</td> <td>14</td> <td>0.035</td> </tr> <tr> <td>VR pre HR</td> <td>-</td> <td>VR post HR</td> <td>-4.612</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann pre HR</td> <td>-</td> <td>VR pre HR</td> <td>4.482</td> <td>14</td> <td>< .001</td> </tr> <tr> <td>Mann post HR</td> <td>-</td> <td>VR post HR</td> <td>-2.688</td> <td>14</td> <td>0.018</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Paired samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>HR BETWEEN GROUPS</caption> <thead> <tr> <th> </th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>Mann pre HR</td> <td>-1.958</td> <td>28</td> <td>0.060</td> </tr> <tr> <td>Mann post HR</td> <td>-1.902</td> <td>28</td> <td>0.068</td> </tr> <tr> <td>VR pre HR</td> <td>-4.013</td> <td>28</td> <td>< .001</td> </tr> <tr> <td>VR post HR</td> <td>-2.344</td> <td>28</td> <td>0.026</td> </tr> <tr> </tr> </tbody> <tbody> <tr> <td><em>Note.</em>  Independent samples student's t-test.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the mannequin.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>128.333</td> <td>10.715</td> <td>SBP pre-simulator</td> <td>134.533</td> <td>11.819</td> <td>-1.870</td> <td>14</td> <td>0.083</td> </tr> <tr> <td>SBP post-mannequin</td> <td>125.600</td> <td>11.648</td> <td>SBP post-simulator</td> <td>131.467</td> <td>14.643</td> <td>-2.094</td> <td>14</td> <td>0.055</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>80.133</td> <td>5.527</td> <td>DBP pre-simulator</td> <td>81.533</td> <td>9.039</td> <td>-0.623</td> <td>14</td> <td>0.544</td> </tr> <tr> <td>DBP post-mannequin</td> <td>78.667</td> <td>6.956</td> <td>DBP post-simulator</td> <td>81.400</td> <td>8.475</td> <td>-2.073</td> <td>14</td> <td>0.057</td> </tr> <tr> <td>HR pre-mannequin</td> <td>92.133</td> <td>14.837</td> <td>HR pre-simulator</td> <td>75.733</td> <td>9.9625</td> <td>6.498</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>87.400</td> <td>9.132</td> <td>HR post-simulator</td> <td>91.400</td> <td>14.217</td> <td>-1.461</td> <td>29</td> <td>0.166</td> </tr> </tbody> </table> <p>SBP = Systolic blood pressure. DBP = Diastolic blood pressure. HR = Heart Rate.</p> <table> <caption>Physiological T-Test results for the participants who started the experiment performing the procedure in the ParaVR simulator.</caption> <thead> <tr> <th>First variable</th> <th>μ</th> <th>σ</th> <th>Second variable</th> <th>μ</th> <th>σ</th> <th>t</th> <th>df</th> <th>p</th> </tr> </thead> <tbody> <tr> <td>SBP pre-mannequin</td> <td>119.067</td> <td>12.898</td> <td>SBP pre-simulator</td> <td>130.600</td> <td>12.188</td> <td>-3.799</td> <td>14</td> <td>0.002</td> </tr> <tr> <td>SBP post-mannequin</td> <td>117.533</td> <td>13.410</td> <td>SBP post-simulator</td> <td>128.200</td> <td>13.385</td> <td>-4.022</td> <td>14</td> <td>0.001</td> </tr> <tr> <td>DBP pre-mannequin</td> <td>76.533</td> <td>8.943</td> <td>DBP pre-simulator</td> <td>80.200</td> <td>6.899</td> <td>-1.815</td> <td>14</td> <td>0.091</td> </tr> <tr> <td>DBP post-mannequin</td> <td>74.333</td> <td>8.541</td> <td>DBP post-simulator</td> <td>79.133</td> <td>7.864</td> <td>-2.003</td> <td>14</td> <td>0.065</td> </tr> <tr> <td>HR pre-mannequin</td> <td>102.067</td> <td>12.876</td> <td>HR pre-simulator</td> <td>91.533</td> <td>11.825</td> <td>4.482</td> <td>29</td> <td>< .001</td> </tr> <tr> <td>HR post-mannequin</td> <td>95.867</td> <td>14.623</td> <td>HR post-simulator</td> <td>103.667</td> <td>14.450</td> <td>-2.688</td> <td>29</td> <td>0.018</td> </tr> </tbody> </table>

opencc-by-4.0Nov 2022View details →

Medical-Waste-4.0-Dataset: v0.1

<p>This dataset was acquired in the framework of the Medical Waste Treating 4.0 funded by the Tuscany Region.</p> <p>The dataset aims to be a valuable resource for devising and testing computer vision methods for the primary sorting of medical waste.</p> <p>Acquisition device: OAK-D camera with tech specs available here https://docs.luxonis.com/projects/hardware/en/latest/pages/BW1098OAK.html</p> <p>Each sample consists of three images, namely an RGB image and a stereo pair:</p> <p>RGB: 1920 x 1080<br> Grayscale: 640 x 400 </p> <p>Example:<br> timestamp.jpg = RGB Image<br> timestamp_r.png = Right image in the stereo pair<br> timestamp_l.png = Left image in the stereo pair</p> <p><br> Categories:<br> - gauze<br> - glove pair latex<br> - glove pair nitrile<br> - glove pair surgery<br> - glove single latex<br> - glove single nitrile<br> - glove single surgery<br> - medical cap<br> - medical glasses<br> - shoe cover pair<br> - shoe cover single<br> - test tube<br> - urine bag</p>

Medication and condition codes used to develop a computable phenotype for Crohn's Disease incident cases

<p>Lists of medication and condition concepts used for Crohn's disease incident case phenotyping as described in my Master's Thesis "Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records From a Large Integrated Health System".</p> <ul> <li>ibd_medication.csv contains medication names, OMOP Concept IDs, RxNorm codes and a flag indicating whether the medication is IBD-specific (i.e., antibiotics and glucocorticoides are marked as unspecific)</li> <li>ibd_conditions.csv contains condition names, OMOP Concept IDs, SNOMED CT codes and a categorical column indicating whether the condition refers to Crohn's Disease (CD), Ulcerative Colitis (UC), or Inflammatory bowel disease unclassified (IBD-U)</li> <li>ibd_symptoms.csv contains symptom names, OMOP Concept IDs, containing symptom group categories, and a flag indicating whether the symptom was added because it is a SNOMED CT descendent code of another code on the list. The list was created based on the IBD symptoms Read Code list provided by Blackwell et. al, 2021, doi:10.1093/ecco-jcc/jjaa146</li> </ul> <p>IBD, Inflammatory Bowel Disease; OMOP, Observational Medical Outcomes Partnership; SNOMED CT, Systematized Nomenclature of Medicine Clinial Terms.</p>

opencc-zeroMar 2023View details →

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Data from: Transforming medical education in Liberia through an international community of inquiry (2017 dataset)

<p>A critical component of building capacity in Liberia's physician workforce involves strengthening the country's only medical school, A.M. Dogliotti College of Medicine. Beginning in 2015, senior health sector stakeholders in Liberia invited faculty and staff from U.S. academic institutions and non-governmental organizations to join a partnership focused on improving undergraduate medical education in Liberia. Over the subsequent six years, the members of this partnership came together through an iterative, mutual-learning process and created what William Torbert et al describe as a "community of inquiry," in which practitioners and researchers pair action and inquiry toward evidence-informed practice and organizational transformation. This community of inquiry developed around a few key institutional and interpersonal relationships but expanded over time. Incorporating faculty, practitioners, and students from Liberia and the U.S., the community of inquiry consistently focused on following the vision, goals, and priorities of leadership in Liberia, irrespective of funding source or institutional affiliation. The work of the community of inquiry has incorporated multiple mixed methods assessments, stakeholder discussions, strategic planning, and collaborative self-reflection, resulting in transformation of M.D. education in Liberia. We suggest that the community of inquiry approach reported here can serve as a model for others seeking to form sustainable, international global health partnerships focused on organizational transformation.</p>

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Data from: Transforming medical education in Liberia through an international community of inquiry (2016 dataset)

<p class="MsoNormal">A critical component of building capacity in Liberia's physician workforce involves strengthening the country's only medical school, A.M. Dogliotti College of Medicine. Beginning in 2015, senior health sector stakeholders in Liberia invited faculty and staff from U.S. academic institutions and non-governmental organizations to join a partnership focused on improving undergraduate medical education in Liberia. Over the subsequent six years, the members of this partnership came together through an iterative, mutual-learning process and created what William Torbert et al describe as a "community of inquiry," in which practitioners and researchers pair action and inquiry toward evidence-informed practice and organizational transformation. This community of inquiry developed around a few key institutional and interpersonal relationships but expanded over time. Incorporating faculty, practitioners, and students from Liberia and the U.S., the community of inquiry consistently focused on following the vision, goals, and priorities of leadership in Liberia, irrespective of funding source or institutional affiliation. The work of the community of inquiry has incorporated multiple mixed methods assessments, stakeholder discussions, strategic planning, and collaborative self-reflection, resulting in transformation of M.D. education in Liberia. We suggest that the community of inquiry approach reported here can serve as a model for others seeking to form sustainable, international global health partnerships focused on organizational transformation.</p>

opencc-zeroMar 2023View details →

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Data from: Transforming medical education in Liberia through an international community of inquiry (2018 dataset)

opencc-zeroMar 2023View details →

The Chilean Waiting List sub-Corpus with medical entities normalized to UMLS terminology

<p>A collection of 2000 medical referrals from the Chilean Waiting List Corpus, manually annotated with six entity types (Finding, Procedure, Disease, Family Member, Body Part, and Medication) and manually normalized to the Unified Medical Language System (UMLS).</p>

openmit-licenseApr 2023View details →

PolyMed: A Medical Dataset Addressing Disease Imbalance for Robust Automatic Diagnosis Systems

<p>We introduce the PolyMed dataset, designed to address the limitations of existing medical case data for Automatic Diagnosis Systems (ADS). ADS assists doctors by predicting diseases based on patients' basic information, such as age, gender, and symptoms. However, these systems face challenges due to imbalanced disease label data and difficulties in accessing or collecting medical data. To tackle these issues, the PolyMed dataset has been developed to improve the evaluation of ADS by incorporating medical knowledge graph data and diagnosis case data. The dataset aims to provide comprehensive evaluation, include diverse disease information, effectively utilize external knowledge, and perform tasks closer to real-world scenarios.</p> <p>We have also made the data collection tools publicly available to enable researchers and other interested parties to contribute additional data in a standardized format. These tools feature a range of customizable input fields that can be selectively utilized according to the user's specific requirements, ensuring consistency and professionalism in the data collection process.</p> <p>All train and test code of our data available in https://github.com/krchanyang/PolyMed</p>

opencc-by-4.0Jul 2023View details →

COVID-19 medical image datasets

<p>This repository contains three curated datasets for the medical image classification described in the paper entitled "Explainable deep transfer learning fine-tunning with domain adaptation enables trustworthy COVID-19 prediction".</p>

opencc-by-4.0Jul 2023View details →

Localized Medical VQA

<p>This dataset contains 3 sub-datasets with questions about regions for the Medical Visual Question Answering (VQA) task. Traditionally, questions are asked about the entire image. In these datasets, we ask questions about randomly generated regions in an image for fundus images as well as cataract surgery frames and surgeries performed with the DaVinci robot.</p><p>All three datasets were created using publicly available datasets.</p><p>For more information and code, visit <a href="https://github.com/sergiotasconmorales/locvqa">our GitHub page.</a></p><p>If you use this dataset, please cite our work:</p><blockquote><p>@inproceedings{tascon2023localized,  <br>title={Localized Questions in Medical Visual Question Answering},  <br>author={Tascon-Morales, Sergio and M{\'a}rquez-Neila, Pablo and Sznitman, Raphael},  <br>booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},  <br>pages={361--370},  <br>year={2023},  <br>organization={Springer} <br>} </p></blockquote>

opencc-by-4.0Sep 2023View details →

Identifying and Aligning Medical Claims Made on Social Media with Medical Evidence

<p>A synthetically generated dataset of medical claims.</p>

I'm not a doctor, but I know the basics: Share information, not medication. (Mimi sio daktari, lakini nafahamu mambo ya msingi. Sambaza taarifa, sio dawa.).

<p>Video in Swahili with English subtitles, containing an example of friends discussing recent experiences of illness and giving advice/information on Antimicrobial Resistance (AMR). </p> <p>Video produced as part of a Participatory Action Research Workshop with young professionals in Mwanza, Tanzania to create public health messages on Antimicrobial Resistance in a post-COVID East Africa in June 2022. This builds off of the data gathered in two international, interdisciplinary research projects (HATUA - Holistic Approaches to Understanding Antimicrobial Resistance in East Africa and CARE - COVID-19 and Antimicrobial Resistance in East Africa – Impact and Response), seeking to understand the wider medical and societal drivers of AMR in East Africa and identify possible interventions to curb the spread of AMR. The workshop ran for 9 days over a 3 week period and consisted of focus group style discussions with participants to explore issues surrounding AMR, antibiotic use, and public health messaging awareness in local communities (Days 1-2),participant-led design of poster, radio, and video messages with feedback from the research team and introduction to filming/recording equipment (days 3-4), filming, shooting and recording materials within local settings in Mwanza with participants serving as actors, directors, and crew with guidance from research team (days 4-8) and a final in-person review and hands-on feedback of preliminary mock-ups of posters and videos (day 9). Participants have continued to collaborate via email and WhatsApp as materials were finalised. </p> <p>Reflexive self-critique: Video and sound quality are a reflection of the participatory approach to producing materials. We are also acknowledge that there are  issues which could have been highlighted in greater detail, particularly considering the post-COVID context e.g., using hand sanitiser and social distancing, for example. Character also emphasises having knowledge despite not being a doctor, which could undermine the importance of other healthcare professionals in spreading critical and reliable public health information. </p> <p>Correspondence: kjf4@st-andrews.ac.uk; mgk@st-andrews.ac.uk</p>

MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

<p><strong>MEDDOPLACE</strong> stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish.</p> <p>This repository includes the corpus' <strong>train and test sets</strong> in multiple formats, as well as the <strong>SNOMED gazetteer</strong>, <strong>cross-mapping</strong> between SNOMED and MeSH and the <strong>multilingual silver standard in 8 languages </strong>(Catalan, English, French, Italian, Dutch, Portuguese, Romanian and Swedish). For more information, please check the attached README file.</p> <p>MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: <a href="https://temu.bsc.es/meddoplace">https://temu.bsc.es/meddoplace</a>.</p> <p> </p> <p><strong>Please cite if you use this resource:</strong></p> <p>Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias and 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. In Procesamiento del Lenguaje Natural, 67. 2021.</p> <pre><code>@article{meddoplace, title={MEDDOPLACE Shared Task overview: recognition, normalization and classification of locations and patient movement in clinical texts}, author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Brivá-Iglesias, Vicent and Gasco-Sanchez, Luis and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, volume = {71}, year={2023}, issn = {1135-5948},<br>DOI = {10.26342/2023-71-23}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561/3961}, pages = {301--311} }</code></pre> <p><strong>Related Links:</strong></p> <p>- MEDDOPLACE website: <a href="https://temu.bsc.es/meddoplace">https://temu.bsc.es/meddoplace</a></p> <p>- MEDDOPLACE overview paper: <a href="http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561">http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561</a></p> <p>- Annotation Guidelines (Spanish): <a href="https://doi.org/10.5281/zenodo.7775234">https://doi.org/10.5281/zenodo.7775234</a></p> <p>- Annotation Guidelines (English): <a href="https://doi.org/10.5281/zenodo.7928145">https://doi.org/10.5281/zenodo.7928145</a></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>

opencc-by-4.0Feb 2023View details →

Dataset for the paper "Exposure of medical students to sexism and sexual harassment and their association with mental health: a cross-sectional study at a Swiss medical school" published in BMJ Open (2023)

<p><strong>Full reference of the paper: </strong></p> <p>Barbier JM, Carrard V, Schwarz J, et al. Exposure of medical students to sexism and sexual harassment and their association with mental health: a cross-sectional study at a Swiss medical school. BMJ Open 2023;13:e069001. doi:10.1136/bmjopen-2022-069001</p>

closedIPD-NOFeb 2026View details →

Non-Opioid Pain Medications After Intracapsular Adenotonsillectomy

ClinicalTrials.gov study NCT04791761. IPD Sharing: NO. Countries: 1. Publications: 10.

controlledIPD-YESFeb 2026View details →

Evaluating the Response to Two Antiretroviral Medication Regimens in HIV-Infected Pregnant Women, Who Begin Antiretroviral Therapy Between 20 and 36 Weeks of Pregnancy, for the Prevention of Mother-to

ClinicalTrials.gov study NCT01618305. IPD Sharing: YES. Countries: 7. Publications: 1.

controlledIPD-YESFeb 2026View details →

Evaluation of Medical Cannabis and Prescription Opioid Taper Support for Reduction of Pain and Opioid Dose in Patients With Chronic Non-Cancer Pain

ClinicalTrials.gov study NCT04827992. IPD Sharing: YES. Countries: 1. Publications: 1.