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545 results for “decision support”
ePneumonia: Development of an Electronic Clinical Decision Support System for Community-Onset Pneumonia
ClinicalTrials.gov study NCT03358342. IPD Sharing: NO. Countries: 1. Publications: 1.
Video Decision Support for Advance Care Planning in Geriatric Patients With Frailty
ClinicalTrials.gov study NCT06671353. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinical Decision Support With Continuous Glucose Monitoring Data for Managing Type 2 Diabetes
ClinicalTrials.gov study NCT07394075. IPD Sharing: YES. Countries: 0. Publications: 8.
Clinical Decision Support System for Quality Assurance in Potassium-Increasing Drug-Drug-Interactions
ClinicalTrials.gov study NCT02020317. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Validation of Artificial Intelligence as Decision Support System in VIA (PRESCRIP-TEC)
ClinicalTrials.gov study NCT06452004. IPD Sharing: YES. Countries: 1. Publications: 2.
Decision Support AMPATH
ClinicalTrials.gov study NCT01235247. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Mixed Methods Study Web-based Life Support Decision Aid
ClinicalTrials.gov study NCT03271658. IPD Sharing: NO. Countries: 1. Publications: 3.
Testing the Decision Aid: Supporting Patient Decisions About Upper Extremity Surgery in Cervical SCI
ClinicalTrials.gov study NCT04995796. IPD Sharing: NO. Countries: 1. Publications: 19.
Patient Centered Clinical Decision Support for Hereditary Cancer Syndromes
ClinicalTrials.gov study NCT06914726. IPD Sharing: YES. Countries: 1. Publications: 6.
Data from: A Decision Support System for assessing management interventions in a Mental Health ecosystem: the case of Bizkaia (Basque Country, Spain)
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Data from: Staffs’ and managers’ perceptions of how and when discrete event simulation modeling can be used as a decision support in quality improvement: a focus group discussion study at two hospital settings in Sweden
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Data from: A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis
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The Threatened Species No-Go Mapping Tool: An online open-access land-use decision support tool that identifies areas of importance for highly sensitive species of conservation concern
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Data from: Wood warblers copy settlement decisions of poor quality conspecifics: support for the tradeoff between the benefit of social information use and competition avoidance
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Supporting data for "Synthesis and Simulation of Ensembles of Boolean Networks for Cell Fate Decision" by Chevalier et al., 2020
<p>Code, data, and notebooks used for the synthesis and simulations of ensembles of Boolean networks for the tumor invasion model introduced in <a href="https://doi.org/10.1371/journal.pcbi.1004571">(Cohen et al, 2015)</a></p> <p>Visualize online:</p> <ul> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3938904/files/Simulations%20-%20Mutant%20analysis.ipynb">Simulations - Mutant analysis.ipynb </a></li> <li> <a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3938904/files/Tumour%20-%20Synthesis%20with%20BoNesis.ipynb">Tumour - Synthesis with BoNesis.ipynb</a></li> </ul> <p>The notebooks can be executed within the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> image 2020-07-01:</p> <pre><code>pip install -U colomoto-docker colomoto-docker -V 2020-07-01 --bind . </code></pre> <p>The synthesis additionally requires executing the following command (within the Docker image):</p> <pre><code>pip install --user bonesis-preview-20200514.zip </code></pre> <p>The ensembles have been generated with the following commands.</p> <pre><code>python synthesis.py synthesis --exact-pkn --globalfps python synthesis.py synthesis --exact-pkn --globalfps --mutant p53 --mutant NICD </code></pre> <p> </p> <ul> </ul>
Data from: Evaluating a handheld decision support device in pediatric intensive care settings
Objective: To evaluate end-user acceptance and the effect of a commercial handheld decision support device in pediatric intensive care settings. The technology, pac2, was designed to assist nurses in calculating medication dose volumes and infusion rates at the bedside. Materials and Methods: The devices, manufactured by InformMed Inc., were deployed in the pediatric and neonatal intensive care units in two health systems. This mixed methods study assessed end-user acceptance, as well as pac2's effect on the cognitive load associated with bedside dose calculations and the rate of administration errors. Towards this end, data were collected in both pre- and post-implementation phases, including through ethnographic observations, semi-structured interviews, and surveys. Results: Although participants desired a handheld decision support tool such as pac2, their use of pac2 was limited. The nature of the critical care environment, nurses' risk perceptions, and the usability of the technology emerged as major barriers to use. Data did not reveal significant differences in cognitive load or administration errors after pac2 was deployed. Discussion and Conclusion: Despite its potential for reducing adverse medication events, the commercial standalone device evaluated in the study was not used by the nursing participants and thus had very limited effect. Our results have implications for the development and deployment of similar mobile decision support technologies. For example, they suggest that integrating the technology into hospitals' existing IT infrastructure and employing targeted implementation strategies may facilitate nurse acceptance. Ultimately, the usability of the design will be essential to reaping any potential benefits.
Data from: Patient factors that influence decision-making in self-management support: a clinical vignette study
Background and aim: Self-management support is an integral part of current chronic care guidelines. The success of self-management interventions varies between individual patients, suggesting a need for tailored self-management support. Understanding the role of patient factors in the current decision making of health professionals can support future tailoring of self-management interventions. The aim of this study is to identify the relative importance of patient factors in health professionals' decision making regarding self-management support. Method: A factorial survey was presented to primary care physicians and nurses. The survey consisted of clinical vignettes (case descriptions), in which 11 patient factors were systematically varied. Each care provider received a set of 12 vignettes. For each vignette, they decided whether they would give this patient self-management support and whether they expected this support to be successful. The associations between respondent decisions and patient factors were explored using ordered logit regression. Results: The survey was completed by 60 general practitioners and 80 nurses. Self-management support was unlikely to be provided in a third of the vignettes. The most important patient factor in the decision to provide self-management support as well as in the expectation that self-management support would be successful was motivation, followed by patient-provider relationship and illness perception. Other factors, such as depression or anxiety, education level, self-efficacy and social support, had a small impact on decisions. Disease, disease severity, knowledge of disease, and age were relatively unimportant factors. Conclusion: This is the first study to explore the relative importance of patient factors in decision making and the expectations regarding the provision of self-management support to chronic disease patients. By far, the most important factor considered was patient's motivation; unmotivated patients were less likely to receive self-management support. Future tailored interventions should incorporate strategies to enhance motivation in unmotivated patients. Furthermore, care providers should be better equipped to promote motivational change in their patients.
Decision Support System for Line-less Assembly Systems Dataset and Results
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Research data supporting: Decreasing alertness modulates perceptual decision-making
<p>The zip file contains research data supporting the paper: Decreasing alertness modulates perceptual decision-making<br> The files are arranged in the following manner:</p> <ol> <li>The raw data is present for 32 subjects used in the analysis.<br> Each subject has a directory, for e.g. s_02 <br> a) Inside this directory, there are two directories:<br> awake: s_02_awake<br> drowsy: s_02_drowsy<br> b) The awake file has 124 trials, and the drowsy has 740 trials<br> c) The files are .mff format (egi) and the coding is as follows:<br> 'tone' - stimulus, 'RESP' - response. <br> Each 'tone' is followed by a 'DIN2' marker, that marks when the stimulus was actually presented.</li> <li> 'summary_Righthanders.csv' contains the other details for all subjects, with subject id and whether the data was<br> actually used for the analysis or not.</li> </ol>
Supplementary material 2 from: Vilizzi L, Piria M, Pietraszewski D, Kopecký O, Špelić I, Radočaj T, Šprem N, Ta KAT, Tarkan AS, Weiperth A, Yoğurtçuoğlu B, Candan O, Herczeg G, Killi N, Lemić D, Szajbert B, Almeida D, Al-Wazzan Z, Atique U, Bakiu R, Chaichana R, Dashinov D, Ferincz Á, Flieller G, Gilles Jr AS, Goulletquer P, Interesova E, Iqbal S, Koyama A, Kristan P, Li S, Lukas J, Moghaddas SD, Monteiro JG, Mumladze L, Olsson KH, Paganelli D, Perdikaris C, Pickholtz R, Preda C, Ristovska M, Švolíková KS, Števove B, Uzunova E, Vardakas L, Verreycken H, Wei H, Zięba G (2022) Development and application of a multilingual electronic decision-support tool for risk screening non-native terrestrial animals under current and future climate conditions. In: Giannetto D, Piria M, Tarkan AS, Zięba G (Eds) Recent advancements in the risk screening of freshwater and terrestrial non-native species. NeoBiota 76: 211-236. https://doi.org/10.3897/neobiota.76.84268
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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.