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251 results for “triage”
Data set supplementing "Benchmarking triage capability of symptom checkers against that of medical laypersons: Survey study"
<p>This is the de-identified data set used to conduct the analyses in the study published as Original Research in the JMIR under the title "Benchmarking triage capability of symptom checkers against that of medical laypersons: Survey study" (https://doi.org/10.2196/24475)</p> <p>The data set contains the assessments of the urgency of symptoms to 45 fictitious clinical case vignettes by 91 US participants, and the participants' age, gender and level of education. Data for the symptom checker apps is needed to fully reproduce our study and can be found in the appendix of the paper "Evaluation of symptom checkers for self diagnosis and triage: audit study" by Semigran et al. (2015) (https://doi.org/10.1136/bmj.h3480).</p>
Intersectionality Spectrum - triage like a hospital
<p>alt-text:</p> <p>Intersectionality spectrum with different categories of intersectionality along the x-axis and the degree of difficulty shown as a bar graph on the y-axis. It shows 3 arrows pointing down on the bars that have the highest degree of difficulty to signify that we need to prioritise support to those who need it most because they have been discriinated the most. It has one green arrow pointing down to those with smaller degrees of difficulty to signify we still need to help those people as well, but with less intensity or frequency. This is similar to how a hospital should triage patients, in that we need to look after the sickest people first.</p>
Supplement to Creating Mobile Self-Triage Applications: Requirements and Usability Perspectives
<p>This is a supplement to our paper "Creating Mobile Self-Triage Applications: Requirements and Usability Perspectives", presented at the <strong>Second International Workshop on Requirements Engineering for Well-Being, Aging, and Health</strong> (REWBAH 2021, https://sites.google.com/view/rewbah2021) and published by IEEE CS.</p> <p>This zip file contains the email template used to invite participants, the consent letter, the pre-test interview, the protocol for the evaluator, the participant tasks, a questionnaire, and a description of three scenarios.</p> <p>Note that the Symptoms Pal application discussed in the paper was named <em>Symptom Checker Mobile</em> (SCM) at the time we performed the usability study.</p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</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/W6EH5l80NmU</p>
Hackathon - TF-TG literature triage Gold Standard corpus
<p>This link contains the gold standard corpus for training the systems aiming at classifying or triaging the documents (PubMed abstracts) in relevant or not relevant for TF-TG relations. Note that this is not purely a curation triage, but we are primarily looking whether the abstracts do describe/characterize TF-TG interactions (positive training set labeled as “1” or they don't (negative training set labeled as “0”). </p> <p>This dataset was manually classified by domain experts into these to classes.</p> <p>Note that in PubMed there is a very considerable class imbalance, implying that there is only a very small fraction of TF-TG relations relevant literature subset. The resulting classifier should be useful to triage the entire PubMed database.</p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562939/files/greekc_triage_training_v02.tsv?download=1">greekc_triage_training_v02.tsv</a></p> </li> <li> <p>Example:</p> </li> </ul> <p>11731616 [{"sourceid":"11731616","sourcedb":"PubMed","text":"Homeobox protein Gsh-1-dependent regulation of the rat GHRH gene promoter. Although GHRH is known to play a pivotal role in the regulation of the GHRH-GH-IGF-I axis, the molecular mechanism of GHRH gene expression has not yet been examined. Here we studied the transcriptional regulation of the GHRH gene 5'promoter using an in vitro experimental model system. We especially focused on the role of homeobox transcriptional factor Gsh-1, because a dwarf phenotype and abolished GHRH expression was observed in Gsh-1 knockout mice. First, we cloned human Gsh-1, which showed 87.3% homology with mouse Gsh-1 at the nucleotide level. When the 5'-promoter region of the rat GHRH gene was introduced into the human placental cell line JEG-3, in which we found the endogenous expression of Gsh-1 as well as GHRH mRNA, substantial transcriptional activity of the promoter was recognized. Promoter activity was further enhanced by overexpression of Gsh-1 protein, whereas it was substantially reduced by elimination of Gsh-1 binding sites. EMSA confirmed the actual binding of Gsh-1 on the multiple binding sites of GHRH gene promoter. Finally, coexpression of CREB-binding protein significantly enhanced the Gsh-1-induced GHRH gene expression, suggesting the cooperative role of the coactivator protein. Because Gsh-1 is found to be expressed in the hypothalamus of the adult rat, our data provide evidence that the Gsh-1 homeobox protein plays a key role in the expression of the GHRH gene.","denotations":[{"obj":"Gene:29446","span":{"begin":84,"end":88}},{"obj":"Gene:288457","span":{"begin":17,"end":22}},{"obj":"Gene:29446","span":{"begin":55,"end":59}},{"obj":"Gene:29446","span":{"begin":146,"end":150}},{"obj":"Gene:29446","span":{"begin":193,"end":197}},{"obj":"Gene:29446","span":{"begin":295,"end":299}},{"obj":"Gene:14842","span":{"begin":430,"end":435}},{"obj":"Gene:14601","span":{"begin":477,"end":481}},{"obj":"Gene:14842","span":{"begin":509,"end":514}},{"obj":"Gene:219409","span":{"begin":553,"end":558}},{"obj":"Gene:14842","span":{"begin":599,"end":604}},{"obj":"Gene:29446","span":{"begin":669,"end":673}},{"obj":"Gene:219409","span":{"begin":783,"end":788}},{"obj":"Gene:2691","span":{"begin":800,"end":804}},{"obj":"Gene:288457","span":{"begin":940,"end":945}},{"obj":"Gene:288457","span":{"begin":1010,"end":1015}},{"obj":"Gene:288457","span":{"begin":1068,"end":1073}},{"obj":"Gene:29446","span":{"begin":1107,"end":1111}},{"obj":"Gene:54244","span":{"begin":1152,"end":1172}},{"obj":"Gene:288457","span":{"begin":1200,"end":1205}},{"obj":"Gene:29446","span":{"begin":1214,"end":1218}},{"obj":"Gene:288457","span":{"begin":1304,"end":1309}},{"obj":"Gene:288457","span":{"begin":1408,"end":1413}},{"obj":"Gene:29446","span":{"begin":1473,"end":1477}}]}] 1</p> <ul> <li> <p>Format: tsv-separated columns (PMID, PubAnnotation JSON formated results of Pubtator for this record together with the automatically detected gene mentions using GnormPlus providing the Entrez Gene Identifiers together with the mention offsets, i.e. start and end character positions, corresponding class label of the records (1= relevant, 0=nonrelevant))</p> </li> <li> <p>PubAnnotation format description: <a href="http://www.pubannotation.org/docs/annotation-format/">http://www.pubannotation.org/docs/annotation-format/</a></p> </li> <li> <p>PubTator record retrieval description:</p> </li> </ul> <p>https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/curl.html</p> <p><strong>Remember:</strong> One must notice that this dataset is high imbalanced, with very few cases of the positive class. The additional files could also be explored to build the model or even to build a classifier working at the level of sentences instead of abstracts,</p> <p> </p>
Hackathon - TF-TG literature triage unlabelled data
<p>Once literature triage system is ready it is time to actually try to apply if to records that do not have any label in order to find the subset that does describe TF-TG interactions (are relevant). This is the corpus that has to be labeled by the systems created (hopefully) during the hackathon. To make the results more useful we have pre-selected records that do mention TFs by exploiting either automatic human TF mention recognition or external references from databases that have manually curated information on transcription factors (from GeneRif or UniProt). This means that these abstracts should be enriched with TF relevant records. This record has the same format as the training data except that the last column with the class label is missing.</p> <p>It contains PMIDs and Abstracts.</p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562913/files/greekc_triage_unlabelled_v01.tsv?download=1">greekc_triage_unlabelled_v01.tsv</a></p> </li> <li> <p>Example:</p> </li> <li> <p>Format: tsv-separated columns (PMID, PubAnnotation JSON formated results of Pubtator for this record together with the automatically detected gene mentions using GnormPlus providing the Entrez Gene Identifiers together with the mention offsets, i.e. start and end character positions</p> </li> <li> <p>PubAnnotation format description: <a href="http://www.pubannotation.org/docs/annotation-format/">http://www.pubannotation.org/docs/annotation-format/</a></p> </li> <li> <p>PubTator record retrieval description:</p> </li> </ul> <p>https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/curl.html</p> <p><strong>Warning:</strong> This file is quite big!</p>
Jamboree - TF-TG literature triage dataset
<p>This file contains the entire set of PMIDs (589,448) that will be used for the manual literature exercise doen using the PubTator interface. The actual instruction of the triage jamboree track are available at:</p> <p><a href="http://greekc.org/wp-content/uploads/2019/02/Abstract_classification_guidelines_TRE_malaga.doc">http://greekc.org/wp-content/uploads/2019/02/Abstract_classification_guidelines_TRE_malaga.doc</a></p> <ul> <li> <p>Name: <a href="https://zenodo.org/record/2562881/files/jamboree_triage_all_PMID_v01.txt?download=1">jamboree_triage_all_PMID_v01.txt</a></p> </li> <li> <p>Example: 17869381</p> </li> <li> <p>Format: one PMID in each line</p> </li> </ul> <p>Smaller subsets will be distributed to each participant in the triage jamboree to carry out a manual triage/classification exercise.</p>
Pandemic Triage Score in Patients With Known or Suspected Severe Acute Respiratory Syndrome (SARS) CoronaVirus (CoV) 2 Infection
ClinicalTrials.gov study NCT04371471. IPD Sharing: YES. Countries: 1. Publications: 7.
Kiosk-Model Self-Triage System in the Pediatric Emergency Department
ClinicalTrials.gov study NCT01515488. IPD Sharing: Not stated. Countries: 1. Publications: 2.
TRIAGE-GS: Towards Reducing Inefficiencies Affecting Genetics Encounters Through Genome Sequencing
ClinicalTrials.gov study NCT06935019. IPD Sharing: YES. Countries: 1. Publications: 1.
Developing a Deliberate Practice Intervention to Recalibrate Physician Heuristics in Trauma Triage
ClinicalTrials.gov study NCT05168579. IPD Sharing: YES. Countries: 1. Publications: 2.
Clinical Predictors of Capillary Refill Time and Their Association With Triage Categories
ClinicalTrials.gov study NCT07054151. IPD Sharing: YES. Countries: 1. Publications: 15.
Effect of Videogames on Real-life Triage Patterns
ClinicalTrials.gov study NCT04516044. IPD Sharing: YES. Countries: 1. Publications: 2.
Triaging and Referring in Adjacent General and Emergency Departments
ClinicalTrials.gov study NCT03793972. IPD Sharing: NO. Countries: 1. Publications: 3.
Risk Stratification in Acute Care: The Meaning of suPAR Measurement in Triage
ClinicalTrials.gov study NCT02643459. IPD Sharing: NO. Countries: 1. Publications: 4.
Evaluation of a mHealth Intervention to Increase Adherence to Triage of Self-collected HPV+ Women (ATICA Project)
ClinicalTrials.gov study NCT03478397. IPD Sharing: Not stated. Countries: 1. Publications: 23.
Clinical Mismatch in the Triage of Wake Up and Late Presenting Strokes Undergoing Neurointervention With Trevo
ClinicalTrials.gov study NCT02142283. IPD Sharing: Not stated. Countries: 5. Publications: 6.
Diagnostic Accuracy of CAD4TB and C-reactive Protein Assay as Triage Tests for Pulmonary Tuberculosis
ClinicalTrials.gov study NCT04666311. IPD Sharing: YES. Countries: 2. Publications: 3.
Role of Methylation Test Triage in HPV Positive Women
ClinicalTrials.gov study NCT06366516. IPD Sharing: YES. Countries: 1. Publications: 8.
Acute Video-oculography for Vertigo in Emergency Rooms for Rapid Triage (AVERT)
ClinicalTrials.gov study NCT02483429. IPD Sharing: Not stated. Countries: 1. Publications: 2.
ScienceDex guides
Understand access before you commit
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International Brain Laboratory public data
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OpenNeuro
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