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5,565 results for “medical”
Dataset for a publication: "Silver-enriched Microdomain Patterns as Advanced Bactericidal Coatings for Polymer-based Medical Devices"
<p>The data set contains the data that were used within the article "Silver-enriched Microdomain Patterns as Advanced Bactericidal Coatings for Polymer-based Medical Devices".</p> <p> </p> <p> </p>
Supplemental Table. Historical cohort of 560 well-described papillary craniopharyngiomas reported in the medical literature (560c).
<p><span>Table </span><span>listing the historical cohort of </span><span>560 </span><span>well-described/illustrated individual papillary craniopharyngiomas reported in the medical literature, including their corresponding references.</span><span><span><span> </span></span></span></p>
Medical publications with information as to whether a publication reports a randomized controlled trial and/or if it covers an oncology topic
<p><strong>Background:</strong></p> <p>Most tools trying to automatically extract information from medical publications are domain agnostic and process publications from any field. However, only retrieving trials from dedicated fields could have advantages for further processing of the data.</p> <p><strong>Dataset collection:</strong></p> <p>A random sample of 900 publications from seven major journals (British Medical Journal, JAMA, JAMA Oncology, Journal of Clinical Oncology, Lancet, Lancet Oncology, New England Journal of Medicine) published between 2010 and 2022 were annotated. Publications that described randomized controlled trials (RCTs) received the label "RCT". Publications that covered oncological topics received the label "ONCOLGY". Trials that fulfilled both criteria were assigned both labels. Trials that were neither RCTs nor covered oncology topics were assigned no label. 100 randomly sampled trials from the New England Journal of Medicine were used as the unseen test set as the journal publishes both oncology and non-oncology articles. </p> <p><strong>Data properties:</strong></p> <p>Each trial is a row in the CSV file. For each trial, there is a doi, a publication date, a title, an abstract, the abstract sections (introduction, methods, results, conclusion), several tags associated with the annotation process (text, _input_hash, _task_hash, options, _view_id, config, accept, answer, _timestamp, _annotator_id,_session_id), and the assigned labels (answer).</p>
Interaction event data logged by the SMASH medication safety dashboard
<p>Interaction event data logged by the SMASH medication safety dashboard from 21 January 2016 to 13 October 2016.</p>
Medical Concept Normalization in Social Media Posts with Recurrent Neural Networks
<p>Text mining of scientific libraries and social media has already proven itself as a reliable tool for<br> drug repurposing and hypothesis generation. The task of mapping a disease mention to a concept<br> in a controlled vocabulary, typically to the standard thesaurus in the Unified Medical Language<br> System (UMLS), is known as medical concept normalization. This task is challenging due to the<br> differences in medical terminology between health care professionals and social media texts coming<br> from the lay public. To bridge this gap, we use sequence learning with recurrent neural networks<br> and semantic representation of one- or multi-word expressions: we develop end-to-end architectures<br> directly tailored to the task, including bidirectional Long Short-Term Memory and Gated Recurrent<br> Units with an attention mechanism and additional semantic similarity features based on UMLS.<br> Our evaluation over a standard benchmark shows that recurrent neural networks improve results<br> over an effective baseline for classification based on convolutional neural networks. A qualitative<br> examination of mentions discovered in a dataset of user reviews collected from popular online health<br> information platforms as well as quantitative evaluation both show improvements in the semantic<br> representation of health-related expressions in social media.</p>
Dataset for Spence et al., "Availability of study protocols for randomized trials published in high-impact medical journals: cross-sectional analysis" (CITATION)
<p>Contains our extraction sheets (as SAS data files), code to calculate the values in the tables in our manuscript, and a supplemental file with additional notes on methods used in our study.</p>
Data for: To Tweet or Not to Tweet, That is the Question: A Randomized Trial of Twitter Effects in Medical Education
<p>This is the raw data for the manuscript: To Tweet or Not to Tweet, That is the Question: A Randomized Trial of Twitter Effects in Medical Education. The abstract for the study is as follows: </p> <p>Introduction: Many medical education journals use Twitter to garner attention for their articles. The purpose of this study was to test the effects of tweeting on article page views and downloads.</p> <p>Methods: The authors conducted a randomized trial using <em>Academic Medicine</em> articles published in 2015. Beginning in February through May 2018, one article per day was randomly assigned to a Twitter (case) or control group. Daily, an individual tweet was generated for each article in the Twitter group that included the title, #MedEd, and a link to the article. The link delivered users to the article’s landing page, which included immediate access to the HTML full text and a PDF link. The authors extracted HTML page views and PDF downloads from the publisher. To assess differences in page views and downloads between cases and controls, a time-centered approach was used, with outcomes measured at 1, 7, and 30 days.</p> <p>Results: In total, 189 articles (94 cases, 95 controls) were analyzed. After days 1 and 7, there were no statistically significant differences between cases and controls on any metric. On day 30, HTML page views exhibited a 63% increase for cases (M=14.72, SD=63.68) when compared to controls (M=9.01, SD=14.34; incident rate ratio=1.63, p=0.01). There were no differences between cases and controls for PDF downloads on day 30.</p> <p>Discussion: Contrary to the authors’ hypothesis, only one statistically significant difference in page views between the Twitter and control groups was found. These findings provide preliminary evidence that after 30 days a tweet can have a small positive effect on article page views.</p>
Self medication among university students in san jose, costa rica databases
<p>These is the database used for the research study "Self medication among university students in San Jose, Costa Rica". There are two csv files, a codified database and a database with legends. Please note legends are in Spanish. If you have any questions, contact natalianorori@gmail.com </p> <p> </p>
Figure 1 in Twenty Years of Complaints: Arthropods of Medical Importance in Maui County, Hawaii, from 2000 to 2019
Figure 1. Maui island divided by study regions used to analyze arthropod pest complaints.
Replication package for 'Medically assisted reproduction and non-normative family forms: legislation and public opinion in Europe'
<p>Replication package for the paper "Medically assisted reproduction and non-normative family forms: legislation and public opinion in Europe", accepted for publication in <em>European Societies</em> (2024). </p> <p>This repository provides the R code to replicate the results. It utilizes data from the European Values Study (available at: https://europeanvaluesstudy.eu/) and an original database on the timing of MAR access legislation for single women and same-sex female couples in Europe. </p> <p> </p> <p> </p>
Large Scale Medical Image Dataset
<p>Multiple dataset from different sources has been aggregated to create a large-scale medical image benchmark dataset in order to measure its performance. As each of the dataset’s images are of different sizes, the images are resized to 3 × 224 × 224 before the training process. This dataset contains total of 35 diseases of 4 different modality and is divided into train, validation, and test with a ratio of 7 : 1 : 2.</p>
pGAN Synthetic Dataset: A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs
<p>Synthetic dataset for <strong>A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs</strong></p> <p><strong> Dataset specification:</strong></p> <ul> <li>MRI images of Vertebral Units labelled based on region</li> <li>Dataset is comprised of 10000 pairs of images and labels</li> <li>Image and label pair number k can be selected by: synthetic_dataset['images'][k] and synthetic_dataset['regions'][k]</li> <li>Images are 3D of size (9, 64, 64)</li> <li>Regions are stored as an integer. Mapping is 0: cervical, 1: thoracic, 2: lumbar</li> </ul> <p>Arxiv paper: <a href="https://arxiv.org/abs/2106.13199">https://arxiv.org/abs/2106.13199</a><br> Github code: <a href="https://github.com/tcoroller/pGAN/">https://github.com/tcoroller/pGAN/</a></p> <p>Abstract:</p> <p>Sharing data from clinical studies can facilitate innovative data-driven research and ultimately lead to better public health. However, sharing biomedical data can put sensitive personal information at risk. This is usually solved by anonymization, which is a slow and expensive process. An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy. As part of the collaboration between Novartis and the Oxford Big Data Institute, we generate a synthetic dataset based on COSENTYX Ankylosing Spondylitis (AS) clinical study. We apply an Auxiliary Classifier GAN (ac-GAN) to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs). The images are conditioned on the VU location (cervical, thoracic and lumbar). In this paper, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties of along three key metrics: image fidelity, sample diversity and dataset privacy.</p>
Knowledge syntheses in medical education: A bibliometric analysis
<p>This is the raw data for the manuscript "Knowledge syntheses in medical education: A bibliometric analysis" by Maggio, Costello, Norton, Driessen, and Artino. The data contain the citations, including the DOI, PMID, and all author locations for all 963 knowledge syntheses included in the study. This study has also been published in a peer reviewed journal: </p> <p>Maggio LA, Costello JA, Norton C, Driessen EW, Artino Jr AR. <a href="https://link.springer.com/article/10.1007/s40037-020-00626-9">Knowledge syntheses in medical education: A bibliometric analysis</a>. <em>Perspectives on Medical Education</em>. 2020 Oct 22:1-9.</p> <p> </p> <p>The abstract for the study is as follows:</p> <p><strong>Purpose</strong> This bibliometric analysis maps the landscape of knowledge syntheses in medical education. It provides scholars with a roadmap for understanding where the field has been and where it might go in the future. In particular, this analysis details the venues in which knowledge syntheses are published, the types of syntheses conducted, citation rates they produce, and altmetric attention they garner.</p> <p><strong>Method</strong> In 2020, the authors conducted a bibliometric analysis of knowledge syntheses published in 14 core medical education journals from 1999 to 2019. To characterize the studies, metadata was extracted from Pubmed, Web of Science, Altmetrics Explorer, and Unpaywall.</p> <p><strong>Results</strong> The authors analyzed 963 knowledge syntheses representing 3.1% of total articles published (n=30,597). On average, 45.9 knowledge syntheses were published annually (SD=35.85, Median=33), and there was an overall 2,620% increase in the number of knowledge syntheses published from 1999 to 2019. The journals each published, on average, a total of 68.8 knowledge syntheses (SD=67.2, Median=41) with <em>Medical Education</em> publishing the most (n=189; 19%). Twenty-one knowledge synthesis types were identified; the most prevalent types were systematic reviews (n=341; 35.4%) and scoping reviews (n=88; 9.1%). Knowledge syntheses were cited an average of 53.80 times (SD=107.12, Median=19) and received a mean Altmetric Attention Score of 14.12 (SD=37.59, Median=6).</p> <p><strong>Conclusions</strong> There has been considerable growth in knowledge syntheses in medical education over the past 20 years, contributing to medical education’s evidence base. Beyond this increase in volume, researchers have introduced methodological diversity in these publications, and the community has taken to social media to share knowledge syntheses. Implications for the field, including the impact of synthesis types and their relationship to knowledge translation, are discussed.</p>
A Pragmatic Trial of Interactive Online Statistical Webtools for Teaching Biostatistics to First Year Medical Students: A Constructivism-Informed Approach
<p>Underlying quantitative and anonymised qualitative data for the study, "A Pragmatic Trial of Interactive Online Statistical Tools for Teaching Biostatistics to First Year Medical Students: A Constructivism-Informed Approach"</p>
A concept for FAIR clinical medication data usage - From care to research with OMOP: literature list of OHDSI studies
<p>This list of papers has been reviewed for the usage of drug data and to answer the question on what drug level the study was done. </p> <p>We checked whether drug ingredient level or drug component with dose and unit was required for the studies. </p>
Racism in Medicine: Impact of Discussion Among Medical Students
<p>Purpose</p> <p>This study evaluated the impact of the Racism in Medicine Summit on student perceptions of various topics related to racism in medicine. The Summit was organized at the Wayne State University School of Medicine (WSUSOM) to educate students, faculty and staff on how structural racism affects the residents of Detroit and the historical relationship between health care and vulnerable populations. The Summit aimed at providing context for what students in Detroit will encounter as physicians-in- training and the skills they will need to master while working within similar communities.</p> <p>Method</p> <p>Qualtrics surveys were created and distributed via email to attendees before and after the event. Responses were obtained via Likert scale and open-text questions.</p> <p>Results</p> <p>A total of 342 out of 445 participants (77%) completed both the pre- and post-survey. Quantitative analysis in post-survey responses revealed more familiarity among participants regarding specific instances of racism in the history of medicine, greater extent of thinking the history of racism impacts present-day Detroit residents, greater extent of thinking that racism influences medical care and/or medical outcomes, and belief that racism is reflected in medical research, compared to pre-survey responses (p < 0.001). Participants also reported more often considering racial or societal influences when learning medicine and more knowledge of what they can do to combat racism as a student and physician (p < 0.001).</p> <p>Qualitative analysis revealed seven themes among participants: the history of racism in</p> <p>Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation</p> <p>medicine, personal reflection, racism in research, bias and microaggression, actions to take against racism, resources for anti-racist education, and racism in medical education.</p> <p>Conclusion</p> <p>Demonstrable changes in medical student attitude and awareness surrounding topics of racism and health care were achieved after the Racism in Medicine Summit. This can serve as a model for other medical schools to raise awareness about racism in medicine.</p>
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part I)
<p>Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf</p> <p>In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:</p> <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> <p>if you found this dataset useful for your research, please cite:</p> <blockquote> <pre>@article{ji2022amos, title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others}, journal={arXiv preprint arXiv:2206.08023}, year={2022} }</pre> </blockquote>
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part II)
<p>Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf</p> <p>In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:</p> <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> <p>if you found this dataset useful for your research, please cite:</p> <blockquote> <pre>@article{ji2022amos, title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others}, journal={arXiv preprint arXiv:2206.08023}, year={2022} </pre> </blockquote>
COMPASS: A magnetic particle-based method for rapid and highly sensitive medical point-of-care diagnostic
<p>This Git consists of all data sets used for generating the figures for the manuscript:</p> <p>COMPASS: A magnetic particle-based method for rapid and highly sensitive medical point-of-care diagnostic</p> <p>Specific information can be find in the readme</p>
Designing and implementing smart glass technology for emergency medical services: A sociotechnical perspective
<p>Objective: This study aims to investigate key considerations and critical factors that influence the implementation and adoption of smart glasses in fast-paced medical settings such as emergency medical services (EMS).</p> <p>Materials and Methods: We employed a sociotechnical theoretical framework and conducted a set of participatory design workshops with fifteen EMS providers to elicit their opinions and concerns about using smart glasses in real practice.</p> <p>Results: Smart glasses were recognized as a useful tool to improve EMS workflow given their hands-free nature and capability of processing and capturing various patient data. Out of the eight dimensions of the sociotechnical model, we found that hardware and software, human-computer interface, workflow, and external rules and regulations were cited as the major factors that could influence the adoption of this novel technology. EMS participants highlighted several key requirements for successful implementation of smart glasses in the EMS context, such as durable devices, easy-to-use and minimal interface design, seamless integration with existing systems and workflow, and secure data management.</p> <p>Discussion: Applications of the sociotechnical model allowed us to identify a range of factors, including not only technical aspects, but also social, organizational, and human factors, that impact the implementation and uptake of smart glasses in EMS. Our work informs design implications for smart glass applications to fulfill EMS providers' needs.</p> <p>Conclusion: The successful implementation of smart glasses in EMS and other dynamic healthcare settings needs careful consideration of sociotechnical issues and close collaboration between different stakeholders.</p>
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.