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5,565 results for “medical”
Medicinal Plants of the Guianas: Medical Guiana
Robert A. DeFilipps, Shirley L. Maina and Juliette Crepin. 2004. Medicinal Plants of the Guianas (Guyana, Surinam, French Guiana). Available online: <p></p>http://botany.si.edu/bdg/medicinal/index.html<p></p>Robert A. DeFilipps, Shirley L. Maina and Juliette Crepin. 2004. Medicinal Plants of the Guianas (Guyana, Surinam, French Guiana). Available online: <p></p>http://botany.si.edu/bdg/medicinal/index.html
Supporting data for "Raising awareness of potential biases in medical machine learning: Experience from a Datathon"
<p>This archive contains files from a Datathon held virtually in February<br>2024 to introduce clinicians and data scientists to the challenge of<br>reviewing a clinical dataset for potential biases.</p>
Opioid medication use and blood DNA methylation: epigenome-wide association meta-analysis
<p>We conducted the first large-scale epigenome-wide meta-analysis of blood DNA methylation and recent use of opioid medications. There were five participating studies (10,842 individuals; 9,886 European ancestry and 956 African ancestry participants) including four that used the newer Illumina EPIC/850K array and one that used the older Illumina 450K array. We identified novel loci differentially methylated in relation to opioid medication use.</p>
Cessation of anti-diabetic medications by 'Daily 2-Only Meals-and- Exercise' lifestyle modification and remission of Type-2 Diabetes Mellitus
<p>This is the dataset describing details of the patient's age, gender, weight, waist circumference, HBA1C levels and Fasting Insulin levels from the date of enrolment in the study and subsequent changes at monthly intervals. </p>
Medical Temporal Constraint Extraction Dataset
<p>This dataset contains 836 drug usage guidelines labeled with medical temporal constraints. These constraints conform to a context-free grammar, allowing their semantics to be computationally represented.</p>
Dataset for Bayesian parametric models for survival prediction in medical applications
<p><strong>Data Source</strong></p> <p>The data for these experiments were derived from these sources:</p> <p>* Hosmer Jr DW, Lemeshow S, May S. Applied Survival Analysis: Regression Modeling of Time-to-Event Data. 2nd ed: John Wiley & Sons; 2008.</p> <p>* Jd K, Prentice R. The statistical analysis of failure time data. New York: John Wiley and Sons; 1980.</p> <p>* Fleming T, Harrington D. Counting Processes and Survival Analysis: John Wiley & Sons; 1991.</p> <p> </p> <p>The raw data was downloaded from web archive.</p> <p>https://web.archive.org/web/20170114043458/http://www.umass.edu/statdata/statdata/data/</p> <p><strong>Contents</strong></p> <p>Each folder contains the original data, a textfile with a description of the data, and the pre-processed version with one-hot encoded variables. An additional YAML file is included with the list of included variables, name of the time and censor variable, name of continuous variables and splitting and partitioning information.</p>
Datasets used in the study "Trends in medication use after the onset of the COVID-19 pandemic in the Republic of Ireland: an interrupted time series study"
<p>This record contains datasets analysed as part of the study "Trends in medication use after the onset of the COVID-19 pandemic in the Republic of Ireland: an interrupted time series study".</p> <p>Two datasets were used, one relating to therapeutic subgroups defined by ATC codes (atc_wide_freq_avg.csv) and one relating to individual medications (drugs_wide_freq_avg.csv). Datasets were collated by combining monthly data reported by HSE Primary Care Reimbursement Services in Ireland relating to dispensing on the General Medical Services scheme at https://www.sspcrs.ie/portal/annual-reporting/</p> <p>Code used to collate datasets and for data management is included in Stata format (compile_data_export_for_analysis_final.do).</p> <p>The study protocol is available at https://doi.org/10.17605/OSF.IO/B4RTM</p>
FastText Spanish Medical Embeddings
<p>[Plan TL/medicine/word embeddings] Word embeddings generated from Spanish corpora that include: (a) the full-text in Spanish available in SciELO.org (until December/2018), (b) all articles from the following Wikipedia categories: Pharmacology, Pharmacy, Medicine and Biology (during December/2018) and (c) the concatenation of the previous two corpora.</p> <p>We used fastText to train the word embeddings.</p> <p>For more information, we refer to the corresponding article: <a href="https://www.aclweb.org/anthology/W19-1916/">https://www.aclweb.org/anthology/W19-1916/</a></p> <p>Copyright (c) 2021 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
MESINESP: Medical Semantic Indexing in Spanish - Train dataset
<p><em><strong>Please use the <a href="https://doi.org/10.5281/zenodo.4612274">MESINESP2 corpus (the second edition of the shared-task)</a> since it has a higher level of curation, quality and is organized by document type (scientific articles, patents and clinical trials).</strong></em></p> <p> </p> <p> </p> <p><strong>INTRODUCTION</strong>:</p> <p>The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) training set has a total of 369,368 records. </p> <p>The training dataset contains all records from LILACS and IBECS databases at the Virtual Health Library (VHL) with a non-empty abstract written in Spanish. The URL used to retrieve records is as follows:<br> http://pesquisa.bvsalud.org/portal/?output=xml&lang=es&sort=YEAR_DESC&format=abstract&filter[db][]=LILACS&filter[db][]=IBECS&q=&index=tw&</p> <p>We have filtered out empty abstracts and non-Spanish abstracts. </p> <p>The training dataset was crawled on 10/22/2019. This means that the data is a snapshot of that moment and that may change over time. In fact, it is very likely that the data will undergo minor changes as the different databases that make up LILACS and IBECS may add or modify the indexes.</p> <p> </p> <p><strong>ZIP STRUCTURE:</strong></p> <p>The training data sets contain 369,368 records from 26,609 different journals. Two different data sets are distributed as described below:</p> <p> - <em>Original Train set</em> with 369,368 records that also include the qualifiers, as retrieved from VHL. <br> - <em>Pre-processed Train set</em><strong> </strong>with the 318,658 records with at least one DeCS code and with no qualifiers. </p> <p> </p> <p> </p> <p><strong>STATISTICS</strong>:</p> <p>Abstracts’ length (measured in characters)<br> Min: 12<br> Avg: 1140.41<br> Median: 1094<br> Max: 9428</p> <p>Number of DeCS codes per file<br> Min: 1<br> Avg: 8.12<br> Median: 7<br> Max: 53</p> <p> </p> <p> </p> <p><strong>CORPUS FORMAT</strong>:</p> <p>The training data sets are distributed as a JSON file with the following format:</p> <pre><code>{ "articles": [ { "id": "Id of the article", "title": "Title of the article", "abstractText": "Content of the abstract", "journal": "Name of the journal", "year": 2018, "db": "Name of the database", "decsCodes": [ "code1", "code2", "code3" ] } ] } </code></pre> <p>Note that the decsCodes field lists the DeCs Ids assigned to a record in the source data. Since the original XML data contain descriptors (no codes), we provide a DeCs conversion table (https://temu.bsc.es/mesinesp/wp-content/uploads/2019/12/DeCS.2019.v5.tsv.zip) with:</p> <p> - DeCs codes<br> - Preferred descriptor (the label used in the European DeCs 2019 set)<br> - List of synonyms (the descriptors and synonyms from both European and Latin Spanish DeCs 2019 data sets, separated by pipes)</p> <p> </p> <p>For more details on the Latin and European Spanish DeCs codes see: http://decs.bvs.br and http://decses.bvsalud.org/ respectively.</p> <p>Please, cite: Krallinger M, Krithara A, Nentidis A, Paliouras G, Villegas M. BioASQ at CLEF2020: Large-Scale Biomedical Semantic Indexing and Question Answering. InEuropean Conference on Information Retrieval 2020 Apr 14 (pp. 550-556). Springer, Cham.</p> <p> </p> <p>Copyright (c) 2020 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
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>
Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages
<p>Data and supplementary information for the paper entitled "Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages" to be published at EMNLP 2015: Conference on Empirical Methods in Natural Language Processing — September 17–21, 2015 — Lisboa, Portugal.</p> <p>ABSTRACT: Previous studies have shown that health reports in social media, such as DailyStrength and Twitter, have potential for monitoring health conditions (e.g. adverse drug reactions, infectious diseases) in particular communities. However, in order for a machine to understand and make inferences on these health conditions, the ability to recognise when laymen's terms refer to a particular medical concept (i.e. text normalisation) is required. To achieve this, we propose to adapt an existing phrase-based machine translation (MT) technique and a vector representation of words to map between a social media phrase and a medical concept. We evaluate our proposed approach using a collection of phrases from tweets related to adverse drug reactions. Our experimental results show that the combination of a phrase-based MT technique and the similarity between word vector representations outperforms the baselines that apply only either of them by up to 55%.</p>
Medical Relation Extraction Gold Standard with CrowdTruth
<p>The lack of annotated datasets for training and benchmarking is one of the main challenges of Clinical Natural Language Processing. In addition, current methods for collecting annotations attempt to minimize disagreement between annotators, and therefore fail to model the ambiguity inherent in language. We propose the <strong>CrowdTruth</strong> method for collecting medical ground truth through crowdsourcing, based on the observation that disagreement between annotators can signal ambiguity in the text, target semantics, or the worker's interpretation.</p> <p>This repository contains a dataset of 3,984 English sentences for medical relation extraction, centering on the cause and treat medical relations, that have been processed with CrowdTruth disagreement analytics to capture ambiguity. In addition, we provide the raw crowdsourcing data used to compile this ground truth, as well as the task templates used to collect the data on CrowdFlower.</p>
Can accurate demographic information about people who use prescription medications non-medically be derived from Twitter?
<p>This archive contains over 3 billion Tweet IDs associated with the paper:<br>"Can accurate demographic information about people who use prescription medications non-medically be derived from Twitter?"</p> <p>The data provides:<br>X (Twitter) IDs of posts included in the study. The IDs can be used to retrieve the original posts via the Twitter API. Posts removed by the original subscribers or whose visibilities are no longer public cannot be retrieved by the poster. </p> <p>Full citation:<br>Yang YC, Al-Garadi MA, Love JS, Cooper HLF, Perrone J, Sarker A. Can accurate demographic information about people who use prescription medications nonmedically be derived from Twitter? Proc Natl Acad Sci U S A. 2023 Feb 21;120(8):e2207391120. doi: 10.1073/pnas.2207391120. Epub 2023 Feb 14. PMID: 36787355; PMCID: PMC9974473.</p> <p>Python scripts related to the analysis are available as supplementary material with the paper. </p> <p>Contact: <br>Abeed Sarker<br>abeed@dbmi.emory.edu</p> <p>Funding:<br>National Institute on Drug Abuse (R01DA057599).</p>
Processing, Spectroscopic and Laboratory Testing Data from a Medical Grade Hot-Melt Extrusion Process
<p>This dataset contains a collection of raw processing data, spectroscopic data, and laboratory test results of medical-grade polymer extrusion experiments. The data was collected in several experiments conducted in a hot-melt extrusion process. The process involved extruding PLA through a slit die and drawing the extruded strands onto spools to obtain the desired dimensional and mechanical properties. The strands were later knitted to form the final medical implant. Throughout the experiments, the extrusion process and equipment were upgraded and refined. Various operational scenarios were simulated under different nozzle configurations. The experiments start using a single-screw extruder and later progress to a double-screw extruder. Medical Grade PURASORB PLA (PLDLA 96/4) material was used when the hardware upgrades were complete. This dataset contains many variations in experimental conditions. However, enough overlap exists to derive working datasets from this compiled raw data.</p> <p> </p> <p>Two working datasets have been derived from this compiled raw data. Using a double-screw extruder, both working Datasets investigate polymer degradation in the hot-melt extrusion process. Both derived datasets are included in this collection.</p> <p> </p> <p>Two Jupyter notebooks are included in this data collection. The first notebook gives an example of how an initial dataset can be derived from the raw data using data science techniques. The second notebook gives an example of how a final dataset can be created from the initial dataset.</p>
Dataset for the paper "Mental Health and Burnout during Medical School: Longitudinal Evolution and Covariates" published in PLOS ONE (2024)
<p><strong>Full reference of the paper:</strong></p> <p>Carrard V, Berney S, Bourquin C, Ranjbar S, Castelao E, Schlegel K, et al. (2024) Mental health and burnout during medical school: Longitudinal evolution and covariates. PLoS ONE 19(4): e0295100. <a href="https://doi.org/10.1371/journal.pone.0295100">https://doi.org/10.1371/journal.pone.0295100</a></p>
Predictors of medical staff's knowledge, attitudes, and behavior of dysphagia assessment: A cross-sectional study
<p>This study aimed to develop training resources and standardize the assessment of dysphagia in patients with stroke. This study was a cross-sectional study. A total of 430 nurses and doctors from four provinces(Guangdong Province, Hunan Province, Guangxi Province, and Shaanxi Province) who were selected by convenience sampling were invited to complete the questionnaire through WeChat, DingTalk, and Tencent QQ from May 23 to 31, 2022. A self-reported questionnaire was used to assess participants' Knowledge, Attitude, and Behavior regarding dysphagia. Participants' sociodemographic, training, and nursing experience were measured using the general information sheet and assessed as potential predictors of medical staff's Knowledge, Attitudes, and Behavior of dysphagia assessment. A multiple linear regression model was used to identify the factors predicting medical staff's Knowledge, Attitudes, and Behavior regarding dysphagia assessment. The mean scores for Knowledge, Attitudes, and Behavior of dysphagia assessments were 92.654(SD 17.519). Multiple linear regression results indicated that experience in dysphagia patients' nursing, related training for dysphagia, working years in the field of dysphagia-related diseases, specialized training in geriatric, swallowing & rehabilitation, and department related to neurology, rehabilitation & elderly were significant predictors, accounting for 35.1% of the variance in scores of medical staff's Knowledge, Attitudes and Behavior of dysphagia assessment. Our findings imply that nursing experience, training, and work for patients with swallowing disorders could have positive effects on the Knowledge, Attitudes, and Behavior of medical staff regarding dysphagia assessment. Hospital administrators should provide relevant resources, such as videos of dysphagia assessment, training centers for the assessment of dysphagia, and swallowing specialist nurses. It is important that health policies fully recognize the role of training and support systems in caring for people with dysphagia.</p>
BioPropaPhenKG on Online Newspapers and Medical Articles
<p><br>The coronavirus disease (COVID-19) spread rampantly around the world at the beginning of 2020 before the governments of each country could prevent it by making decisions based on medical data analysis. With proper formalization, the terabytes of new textual data available online every day could have been used for the early description and detection of cases of this virus. Since then, the number of Event-Based Surveillance (EBS) applications has increased exponentially. These applications aim to mine channels of unstructured information to detect signs of possible public health events' progression. However, one problem with such systems is the need for expert intervention to define which event will be captured, which relevant terms should be used in the search, and to analyze the events to modify the search procedure constantly. Another problem is that many of these applications do not consider both spatial and temporal characteristics. Addressing such limitations, this datasets presents a novel approach. We propose the use of BioPropaPhenKG to replace such systems. In this dataset, BioPropaPhen was enhanced with information comming from unstructured texts from online newspapers and medical articles. BioPropaPhenKG, its ontology and other useful information can be found in <a href="../records/10911980">https://zenodo.org/records/10911980</a>. The code used for this use case can be found in <a href="https://github.com/Gabriel382/DDPF-Health-Risks">https://github.com/Gabriel382/DDPF-Health-Risks</a> . Finally, the datasets used where UMLS MetamorphoSys, OpenStreetMaps, Wikidata, <a href="https://aylien.com/blog/free-coronavirus-news-dataset">Aylien</a> (data only from November of 2019) and <a href="https://allenai.org/data/cord-19">CORD-19</a> (data only from December of 2019). </p> <p> </p> <p>To read, you just need to load it with Neo4j:4.4.3. Alternatively, you can open it with docker using the following command: </p> <p>docker run --interactive --tty --rm \<br> --publish=7474:7474 --publish=7687:7687 \<br> --volume=/path-to-data-folder:/data --user="$(id -u):$(id -g)"\<br> neo4j:4.4.3 \<br>neo4j-admin load --from=/data/BioPropaPhenKG-Journal-Medical.dump --database "neo4j" --force</p> <p> </p>
Perceived academic stress, hair and saliva cortisol concentrations, and their relationship with anthropometric measures associated with obesity in first-year medical students.
<p>Cortisol plays an important role between stress, weight gain, and the development of obesity. Therefore, we investigated the association between stress, eating behavior, cortisol, and anthropometric measures related to obesity in a sample of medical students. We determined cortisol concentrations by ELISA and related it to self-reported stress, eating behavior, and anthropometric measurements throughout the academic period. We report an increase in hair cortisol, higher self-reported stress scores, and BMI mainly in females. Finally, we found evidence of positive associations between capillary cortisol and BMI. Also, eating behavior is affected by perceived stress mainly in females.</p> <p>In this database, we provide weight variables, BMI, psychometric tests, and hormonal determinations.</p> <p><br> You can see two sheets in the Excel file. Sheet 1 includes the raw data and sheet 2 includes a description of each variable, as well as the bibliography (article or book) where each survey or protocol was obtained.</p> <p><br> In addition, the data specify the units of the hormonal variables, as well as the units of the anthropometric variables. If you have questions about the interpretation of psychometric test scores, you can contact our team for further details.</p> <p> </p>
Reliability of citations of medRxiv preprints in articles published on COVID-19 in the world leading medical journals
<p>Articles published on COVID in 2020 in the BMJ, The Lancet, the JAMA and the NEJM were manually screened to identify all articles citing at least one preprint from medRxiv. We searched PubMed, Google and Google Scholar to assess if the preprint had been published in a peer-reviewed journal, and when. Published articles were screened to assess if the title, data or conclusions were identical to the preprint version.</p>
Medical Education Journal Data and Supplemental Files (2000 - 2020)
<p>This is the supplemental data, figures, and tables for <em>The Voices of Medical Education Science: Describing the Landscape</em>. This also includes the author thesaurus and institution thesaurus with supporting read me files. </p> <p><strong>Abstract</strong> </p> <p>Introduction</p> <p>Medical education has been described as a dynamic and growing field, driven in part by its unique body of scholarship. The voices of authors who publish medical education literature have a powerful impact on the discourses of the community. While there have been numerous studies looking at aspects of this literature, there has been no comprehensive view of recent publications.</p> <p>Method</p> <p>The authors conducted a bibliometric analysis of all articles published in 24 medical education journals published between 2000-2020 to identify article characteristics, with an emphasis on author gender, geographic location, and institutional affiliation. This study replicates and greatly expands on two previous investigations by examining all articles published in these core medical education journals. </p> <p>Results </p> <p>The journals published 37,263 articles with the most articles published in 2020 (n=3,957, 10.7%) and least in 2000 (n=711, 1.9%) representing a 456.5% increase. The articles were authored by 139,325 authors of which 62,708 were unique. Men were more prevalent across all authorship positions (n=62,828; 55.7%) than women (n=49,975; 44.3%). Authors listed 154 country affiliations with the United States (n=42,236, 40.4%), United Kingdom (n=12,967, 12.4%), and Canada (n=10,481, 10.0%) most represented. Ninety-three countries (60.4%) were low- or middle-income countries accounting for 9,684 (9.3%) author positions. Few articles were written by multinational teams (n=3,765; 16.2%). Authors listed affiliations with 4,372 unique institutions. Across all author positions, 48,189 authors (46.1%) were affiliated with institutions ranked globally as Top 200 institutions by the Times Higher Education ranking. </p> <p>Discussion </p> <p>There is a relative imbalance of author voices in medical education. If the field values a diversity of perspectives, there is considerable opportunity for improvement.</p>
ScienceDex guides
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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.