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545 results for “decision support”
Clinical Decision Support for Opioid Use Disorders in Medical Settings: Usability Testing in an EMR
ClinicalTrials.gov study NCT03559179. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Understanding farmers' reasons behind mitigation decisions is key in supporting their coexistence with wildlife
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Anonymized source data files for figures in: Recurrent processes support a cascade of hierarchical decisions
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Data from: Does a decision support tool designed to depict West Nile Virus risk explain variation in ruffed grouse (Bonasa umbellus) use of managed forests?
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Vulnerability of sea turtle nesting sites to erosion and inundation: a decision support framework to maximize conservation
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Data to support "Plastic Rivers project: Consumer-Based Actions to Reduce Plastic Pollution in Rivers: a Multi-Criteria Decision Analysis Approach"
Focusing on the most commonly occurring consumer plastic items present in European freshwater environments, we identified and evaluated consumer-based actions with respect to their direct or indirect potential to reduce macroplastic pollution in freshwater environments. As the main end users of these items, concerned consumers are faced with a bewildering array of choices to reduce their plastics footprint, notably through recycling or using reusable items. Using a Multi-Criteria Decision Analysis approach, we explored the effectiveness of 27 plastic reduction actions with respect to their feasibility, economic impacts, environmental impacts, unintended social/environmental impacts, potential scale of change and evidence of impact. Total action scores have been calculated using a multi-criteria decision analysis (MCDA) on 27 plastic reduction actions identified though a literature review. Each of the total action scores is the weighted sum of 1-5 scores (assigned to each of ten criteria to assess the positive environmental impact of each action, based on the literature review) and % weights of each criterion to rank their relative importance. We provide two tables in csv format: 1) the % weights assigned by 15 experts to rank the relative importance of socio-economic and environmental criteria to assess plastic reduction actions; 2) the 1-5 scores assigned to each of the ten criteria in relation to each of the 27 actions; the median weights for each of the ten criteria calculated from 1); and the total action scores of each of the 27 actions calculated as weighted sum (e.g. TAS action 1: sum of ten products of % criterion weight * 1-5 scores assigned to each combination criterion - plastic reduction action).
Data from: A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis
Objective: A Clinical Decision Support System (CDSS) that can amass Electronic Health Record (EHR) and other patient data holds promise to provide accurate classification and guide treatment choices. Our objective is to develop the Decision Support System for Making Personalized Assessments and Recommendations Concerning Breast Cancer Patients (DPAC), which is a CDSS learned from data that recommends the optimal treatment decisions based on a patient's features. Method: We developed a Bayesian network architecture called Causal Modeling with Internal Layers (CAMIL), and an algorithm called Treatment Feature Interactions (TFI), which learns from data the interactions needed in a CAMIL model. Using the TFI algorithm, we learned interactions for six treatments from the Lynn Sage Data Set (LSDS). We created a CAMIL model using these interactions, resulting in a DPAC which recommends treatments towards preventing 5-year breast cancer metastasis. Results: In a 5-fold cross-validation analysis, we compared the probability of being metastasis free in 5 years for patients who made decisions recommended by DPAC to those who did not. These probabilities are (the probability for those making the decisions appears first): chemotherapy (.938, .872); breast/chest wall radiation (.939, .902); nodal field radiation (.940, .784); antihormone (.941, .906); HER2 inhibitors (.934, .880); neadjuvant therapy (.931, .837). In an application of DPAC to the independent METABRIC dataset, the probabilities for chemotherapy were (.845, .788). Discussion: Patients who took the advice of DPAC had, as a group, notably better outcomes than those who did not. We conclude that DPAC is effective at amassing and analyzing data towards treatment recommendations. Some of the findings in DPAC are controversial. For example, DPAC says that chemotherapy increases the chances of metastasis for many node negative patients. This controversy shows the importance of developing a conclusive version of DPAC to ensure we provide patients with the best patient-specific treatment recommendations.
Data from: A Decision Support System for assessing management interventions in a Mental Health ecosystem: the case of Bizkaia (Basque Country, Spain)
Evidence-informed strategic planning is a top priority in Mental Health (MH) due to the burden associated with this group of disorders and its societal costs. However, MH systems are highly complex, and decision support tools should follow a systems thinking approach that incorporates expert knowledge. The aim of this paper is to introduce a new Decision Support System (DSS) to improve knowledge on the health ecosystem, resource allocation and management in regional MH planning. The Efficient Decision Support-Mental Health (EDeS-MH) is a DSS that integrates an operational model to assess the Relative Technical Efficiency (RTE) of small health areas, a Monte-Carlo simulation engine (that carries out the Monte-Carlo simulation technique), a fuzzy inference engine prototype and basic statistics as well as system stability and entropy indicators. The stability indicator assesses the sensitivity of the model results due to data variations (derived from structural changes). The entropy indicator assesses the inner uncertainty of the results. RTE is multidimensional, that is, it was evaluated by using 15 variable combinations called scenarios. Each scenario, designed by experts in MH planning, has its own meaning based on different types of care. Three management interventions on the MH system in Bizkaia were analysed using key performance indicators of the service availability, placement capacity in day care, health care workforce capacity, and resource utilisation data of hospital and community care. The potential impact of these interventions has been assessed at both local and system levels. The system reacts positively to the proposals by a slight increase in its efficiency and stability (and its corresponding decrease in the entropy). However, depending on the analysed scenario, RTE, stability and entropy statistics can have a positive, neutral or negative behaviour. Using this information, decision makers can design new specific interventions/policies. EDeS-MH has been tested and face-validated in a real management situation in the Bizkaia MH system.
Data set supplementing "Interactive versus static decision support tools for COVID-19: An experimental comparison"
<p>This is the de-identified data set used to conduct the analyses in the preprint submitted to JMIR Public Health and Surveillance under the title "Interactive versus static decision support tools for COVID-19: An experimental comparison" (https://doi.org/10.2196/preprints.33733).</p> <p>This data set contains the appraisal of 196 respondents (without decision support, with static or with interactive decision support) to appropriate social and care-seeking behavior for seven fictitious descriptions of patients. Additionally, this data contains participants'</p> <ul> <li>gender</li> <li>educational background</li> <li>previous medical training</li> <li>affinity for technology</li> <li>experience with COVID-19 related medical questions</li> <li>perceived threat from COVID-19</li> <li>answers to a COVID-19 knowledge test</li> <li>accuracy</li> <li>decision certainty (after deciding)</li> <li>mental effort</li> <li>ratings of <ul> <li>the decision support tool's usefulness </li> <li>ease of use</li> <li>trust</li> <li>future intention to use the tool</li> </ul> </li> </ul>
Dataset (MATLAB format) from Yang et al (2022) Thalamus-driven functional populations in frontal cortex support decision-making. Nat. Neurosci.
<p><strong>Summary</strong></p> <p>These experiments measure neuronal responses from the left hemisphere of premotor cortex (anterior lateral motor cortex, ALM) of adult mice performing pole location discrimination with a short-term memory. In a subset of the recordings, we inactivate activity of one of the brain regions providing inputs to ALM (ipsilateral S1/S2, contralateral ALM, and ipsilateral Thal<sub>ALM</sub>) in some trials.</p> <p>This dataset contains data from 9626 single units, 73 mice, 347 sessions. The dataset is described as “the primary dataset” in the paper below. The experiments (including experiment methods) are described in the paper.</p> <p><em>Yang W, Tipparaju SL, Chen G, Li N, (2022). Thalamus-driven functional populations in frontal cortex activity supports decision-making. Nat Neurosci, in press.</em></p> <p> </p> <p>The second dataset used in the paper can be downloaded also from Zenodo at</p> <pre><a href="https://doi.org/10.5281/zenodo.6713616">https://doi.org/10.5281/zenodo.6713616</a></pre> <p> </p> <p><strong>How to cite the data</strong></p> <p>If you publish any work using the data, please cite the Chen et. al., (2021) publication above and also cite the dataset in the following recommended format:</p> <p>Li N (2022); Data and simulations related to: Thalamus-driven functional populations in frontal cortex activity supports decision-making. Yang et al (2022) Nat Neurosci.</p> <p><a href="http://dx.doi.org/10.5281/zenodo.6846161">http://dx.doi.org/10.5281/zenodo.6846161</a></p> <p> </p> <p><strong>How to get started</strong></p> <p>Once downloaded</p> <p>1) unzip “<strong>func</strong>”</p> <p>2) unzip "<strong>scripts</strong>"</p> <p>3) Run any scripts "<strong>demo_*.m</strong>" within "<strong>scripts</strong>"</p> <p> </p> <p>A collection of analyses scripts that reproduce figures in " Yang et al (2022)" is included in the folder "<strong>\scripts\</strong>"</p> <p><strong>demo_compute_activity_modes_independentTrials.m</strong> – plot t-SNE embedding and all the response profile clusters; plots PSTHs from an example cluster; plots neurons connected to S1/S2, cALM, ThalALM on the t-SNE.</p> <p><strong>demo_compute_tSNE_embedding.m</strong> – t-SNE embedding of neuronal response profiles (based on PSTH shape)</p> <p><strong>demo_compute_activity_modes.m</strong> – compute activity modes from neuronal population responses.</p> <p><strong>demo_compute_selectivity_vector_stability.m</strong> – analysis of selectivity vectors</p> <p> </p> <p> </p>
Datasets used in the papers: STree: A Single Multi-class Oblique Decision Tree Based on Support Vector Machines & ODTE - An ensemble of multi-class SVM-based oblique decision trees
<p>These are the 49 datasets used in the benchmark. 45 of them are from the UCI machine learning repository, while the other 4 correspond to a problem about fecundity estimation for fisheries</p>
Data and results for the paper "Decision Support for the Technician Routing and Scheduling Problem"
<p>Data and results for the paper "Decision Support for the Technician Routing and Scheduling Problem"</p>
Supplementary material 1 from: Seehausen ML, Branco M, Afonso C, Kenis M (2023) Testing a modified version of the EPPO decision-support scheme for release of classical biological control agents of plant pests using Ganaspis cf. brasiliensis and Cleruchoides noackae as case studies. NeoBiota 87: 121-141. https://doi.org/10.3897/neobiota.87.103187
Decision-support scheme for release of classical biological control agents of plant pests – Environmental impact assessment (EIA)
Decision Support System for Anesthetists
ClinicalTrials.gov study NCT04079036. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Artificial Intelligence Diagnostic Decision Support to Reduce Antimicrobial Prescriptions in Young Children With Colds
ClinicalTrials.gov study NCT06876259. IPD Sharing: NO. Countries: 1. Publications: 4.
Learning Implementation of Guideline-based Decision Support System for Hypertension Treatment: Testing Alternative Antihypertensive Regimens Using ACE-Inhibitors, Calcium Channel Blockers and Diuretic
ClinicalTrials.gov study NCT03587103. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
The Effectiveness of a Decision-Support Tool for Adult Consumers With Mental Health Needs and Their Care Managers
ClinicalTrials.gov study NCT02761733. IPD Sharing: NO. Countries: 0. Publications: 5.
Clinical Decision Support (CDS) for Radiology Imaging
ClinicalTrials.gov study NCT02996045. IPD Sharing: NO. Countries: 1. Publications: 4.
Decision Support System to Evaluate VENTilation in ARDS
ClinicalTrials.gov study NCT04115709. IPD Sharing: NO. Countries: 3. Publications: 2.
Clinical Decision Support for Blood Transfusions to Improve Guideline Adherence
ClinicalTrials.gov study NCT05634005. IPD Sharing: NO. Countries: 1. Publications: 1.
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.