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447 results for “Model validation”
Validation of the predictive accuracy of health-state utility values based on the Lloyd model for metastatic or recurrent breast cancer in Japan
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Data from: Simulation-based validation of spatial capture-recapture models: a case study using mountain lions
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Data from: Community science validates climate suitability projections from ecological niche modeling
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TTnet - Model validation data
<p>This repository contains the raw data collected to validate the <em>TTnet</em> time and energy model, which is described in</p> <ul> <li><strong>Time-Triggered Wireless Architecture</strong><br> Romain Jacob, Licong Zhang, Marco Zimmerling, Samarjit Chakraborty, Lothar Thiele <br> Accepted to ECRTS 2020 <br> <a href="https://arxiv.org/abs/2002.07491">arxiv.org/abs/2002.07491</a></li> <li><strong>Leveraging Synchronous Transmissions for the Design of Real-time Wireless Cyber-Physical Systems</strong><br> Romain Jacob<br> Doctoral dissertation , 2020<br> <a href="https://doi.org/10.5281/zenodo.3510184">10.5281/zenodo.3510184 </a>(Chapter 5)</li> </ul> <p>The processing of these data is described in detailed in the following GitHub repository:<br> <a href="https://github.com/romain-jacob/TTW-Artifacts">github.com/romain-jacob/TTW-Artifacts</a></p> <p><strong>Files description</strong></p> <ul> <li><strong>data_raw.zip</strong><br> Contains the serial logs and test configuration files, for all three test series</li> <li><strong>serieX_all_data.zip</strong><br> Contains all the test results (serial logs + GPIO traces + power traces) for the test of series X.<br> Missing for series 3 (we forgot to save it before it got deleted from the server... sorry about that)</li> </ul>
Development and validation of a machine learning model for use as an automated artificial intelligence tool to predict mortality risk in patients with COVID-19
<p><strong>Background</strong></p> <p>New York City quickly became an epicenter of the COVID-19 pandemic. Due to a sudden and massive increase in patients during COVID-19 pandemic, healthcare providers incurred an exponential increase in workload which created a strain on the staff and limited resources. As this is a new infection, predictors of morbidity and mortality are not well characterized.</p> <p><strong>Methods</strong></p> <p>We developed a prediction model to predict patients at risk for mortality using only laboratory, vital and demographic information readily available in the electronic health record on more than 3000 hospital admissions with COVID-19. A variable importance algorithm was used for interpretability and understanding of performance and predictors.</p> <p><strong>Findings</strong></p> <p>We built a model with 84-97% accuracy to identify predictors and patients with high risk of mortality, and developed an automated artificial intelligence (AI) notification tool that does not require manual calculation by the busy clinician. Oximetry, respirations, blood urea nitrogen, lymphocyte percent, calcium, troponin and neutrophil percentage were important features and key ranges were identified that contributed to a 50% increase in patients’ mortality prediction score. With an increasing negative predictive value (NPV) starting 0.90 after the second day of admission, we are able more confidently able identify likely survivors. This study serves as a use case of a model with visualizations to aide clinicians with a better understanding of the model and predictors of mortality. Additionally, an example of the operationalization of the model via an AI notification tool is illustrated.</p>
Experimental and Simulation Results of "Hemodynamic study in 3D printed stenotic coronary artery models: experimental validation and transient simulation"
<p>This repository contains the experimental and simulation results of the submitted article "Hemodynamic study in 3D printed stenotic coronary artery models: experimental validation and transient simulation" by Carvalho, V., Rodrigues, N., Ribeiro, R., Costa, P., Lima, R., Teixeira, S. </p>
Validation results of Non-parametric models of pH Neutralization plant Dataset
<p>This repository contains a table with different validation results of a series of models tested in pH Neutralization plant and Clarke plant.</p> <p>In this case, the naming protocol was as follows: [plant name]_DataSet_[model variable].</p> <p>The content of every CSV file is presented as follows: </p> <p>Column 1: { name: Model_No, type: numeric integer, limit: None, description: Code to identifiy the model (Primary Key)}</p> <p>Column 2: { name: KS, type: numeric float, limits: [0 1], description: Kolgomorov-Smirnov test}</p> <p>Column 3: { name: AD, type: numeric float, limits: [0 1], description: Anderson-Darling test}</p> <p>Column 4: { name: SW, type: numeric float, limits: [0 1], description: Shapiro-Wilk test}</p> <p>Column 5: { name: WX, type: numeric float, limits: [0 1], description: Wilcoxon test}</p> <p>Column 6: { name: FIT, type: numeric float, limits: [-Inf 1], description: Goodness of Fit metric}</p> <p>Column 7: { name: TIC, type: numeric float, limits: [0 1], description: Theil Inequality Index}</p> <p>Column 8: { name: Willmott, type: numeric float, limits: [0 1], description: Willmott metric}</p> <p>Column 9: { name: Russell_Pr, type: numeric float, limits: [0 1], description: Phase of Russell }</p> <p>Column 10: { name: Russell_Mr, type: numeric float, limits: [0 Inf], description: Magnitude of Russell }</p> <p>Column 11: { name: SG_Mr, type: numeric float, limits: [-Inf 1], description: Sprague & Geers}</p> <p>Column 12: { name: Anova, type: numeric float, limits: [0 1], description: Anova test}</p> <p>Column 13: { name: Dvure, type: numeric float, limits: [0 100], description: Dvurecenska metric}</p> <p>Column 14: { name: DTW, type: numeric float, limits: [0 Inf], description: DTW metric}</p> <p>Column 15: { name: D_DTW, type: numeric float, limits: [0 Inf], description: derivative of DTW metric}</p>
Data from: Validating two-dimensional leadership models on three-dimensionally structured fish schools
Identifying leader-follower interactions is crucial for understanding how a group decides where or when to move, and how this information is transferred between members. Although many animal groups have a three-dimensional structure, previous studies investigating leader-follower interactions have often ignored vertical information. This raises the question whether commonly used two-dimensional leader-follower analyses can be used justifiably on groups that interact in three dimensions. To address this we quantified the individual movements of banded tetra fish (Astyanax mexicanus) within shoals by computing the three-dimensional trajectories of all individuals using a stereo-camera technique. We used these data firstly to identify and compare leader-follower interactions in two and three dimensions, and secondly to analyse leadership with respect to an individual's spatial position in three dimensions. We show that for 95% of all pairwise interactions leadership identified through two-dimensional analysis matches that identified through three-dimensional analysis, and we reveal that fish attend to the same shoalmates for vertical information as they do for horizontal information. Our results therefore highlight that three-dimensional analyses are not always required to identify leader-follower relationships in species that move freely in three-dimensions. We discuss our results in terms of the importance of taking species' sensory capacities into account when studying interaction networks within groups.
Fractal geometry features of aerosol particle and its contribution to atmospheric optical property: development of Fractal Aerosol Cluster Model and its validation of atmospheric visibility during a heavy haze event
<p>-------------------------<br>Content of the dataset<br>-------------------------<br>****** the experiment case (EXP) ; the control case (CTR) ******</p> <p>1. Meteorological elements.tar contains observational and simulated data for T2, WS, RH, and PM2.5 time series, which can be used to plot Figure 4 and build Table 2</p> <p>2. Planar distribution.tar contains the horizontal spatial distribution data of aerosol extinction coefficients simulated by CTR and EXP for the four typical moments selected in this paper, which can be used to plot Figures 5, 6, and 7</p> <p>3. PM.rar contains the vertical profile data of simulated Particulate Matter concentrations by CTR and EXP during the study period in the paper, which can be utilized for drawing Fig. 11.</p> <p>4. Timeseries.tar contains observational and simulated data for time series of atmospheric visibility and surface shortwave radiation, which can be used to plot Figures 5, 6, 7, 8, S1, and build Table 3</p> <p>5. wrfbiochemi.rar contains the biogenic emissions data for simulation both for CTR and EXP.</p> <p>6. wrffirechemi.rar contains the biomass burning emissions data for simulation both for CTR and EXP.</p> <p>7. wrfchemi.rar contains the Anthropogenic emissions data for simulation both for CTR and EXP.</p> <p>8. The file module_optical_averaging.F contains the main code of the improved visibility model, the Fractal Aerosol Cluster Model</p> <p>(FACM), which is coupled to WRF-Chem and used by EXP. It is located in the chem/ directory and called by optical_driver.F.</p> <pre> </pre> <p> </p> <p>-------------------------</p> <p>Contact information</p> <p>-------------------------</p> <p> </p> <p>Zhenxin Liu</p> <p>liuzhenxin@nuist.edu.cn</p> <p> </p> <p> </p>
Updated SAUUHUPP Model and Empirical Validations
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External validation of EPIC's Risk of Unplanned Readmission model, the LACE+ index and SQLape® as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in Switzerland
<p>Introduction: Readmissions after an acute care hospitalization are relatively common, costly to the health care system, and are associated with significant burden for patients. As one way to reduce costs and simultaneously improve quality of care, hospital readmissions receive increasing interest from policy makers. It is only relatively recently that strategies were developed with the specific aim of reducing unplanned readmissions using prediction models to identify patients at risk. EPIC's Risk of Unplanned Readmission model promises superior performance. However, it has only been validated for the US setting. Therefore, the main objective of this study is to externally validate the EPIC's Risk of Unplanned Readmission model and to compare it to the internationally, widely used LACE+ index, and the SQLAPE® tool, a Swiss national quality of care indicator.</p> <p>Methods: A monocentric, retrospective, diagnostic cohort study was conducted. The study included inpatients, who were discharged between the 1<sup>st</sup> of January 2018 and the 31<sup>st</sup> of December 2019 from the Lucerne Cantonal Hospital, a tertiary-care provider in Central Switzerland. The study endpoint was an unplanned 30-day readmission. Models were replicated using the original intercept and beta coefficients as reported. Otherwise, score generator provided by the developers were used. For external validation, discrimination of the scores under investigation were assessed by calculating the area under the receiver operating characteristics curves (AUC). Calibration was assessed with the Hosmer-Lemeshow <i>X</i><sup><i>2</i></sup><span><span></span></span> goodness-of-fit test This report adheres to the TRIPOD statement for reporting of prediction models.</p> <p>Results: At least 23,116 records were included. For discrimination, the EPIC´s prediction model, the LACE+ index and the SQLape® had AUCs of 0.692 (95% CI 0.676-0.708), 0.703 (95% CI 0.687-0.719) and 0.705 (95% CI 0.690-0.720). The Hosmer-Lemeshow <i>X</i><sup><i>2</i></sup><span><span></span></span> tests had values of p<0.001.</p> <p>Conclusion: In summary, the EPIC´s model showed less favorable performance than its comparators. It may be assumed with caution that the EPIC´s model complexity has hampered its wide generalizability - model updating is warranted.</p>
Reproducibility material for ground motion validation of hybrid models
<p>Code and data repository to reproduce research on the effect of model hybridization on ground motion prediction.</p>
Dataset accompanying: "Applying and Validating Coulomb Rate-and-State Seismicity Models in Acoustic Emission Experiments")
<p>Dataset accompanying: "Applying and Validating Coulomb Rate-and-State Seismicity Models in Acoustic Emission Experiments" by Heimisson, Naderloon, Chandra and Barnhoorn.</p> <p>Please reference the following publication if the data is used:<br><br>Heimisson, E.R., Naderloo, M., Chandra, D. and Barnhoorn, A., 2024. Applying and validating Coulomb rate-and-state seismicity models in acoustic emission experiments. <em>Tectonophysics</em>, p.230574. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.tecto.2024.230574" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.tecto.2024.230574 </span></span></a></p>
Figure 5 from: Tzankova D, Peikova L, Vladimirova S, Georgieva M (2019) Development and validation of RP-HPLC method for stability evaluation of model hydrazone, containing a pyrrole ring. Pharmacia 66(3): 127-134. https://doi.org/10.3897/pharmacia.66.e47035
Figure 5 Chromatogram of the separated mixture of the analyzed hydrazone D-5d (tR = 6.800) and its possible degradation products – the hydrazide D-5 (tR = 4.387) and the corresponding aldehyde d (tR = 1.387).
Figure 7 from: Tzankova D, Peikova L, Vladimirova S, Georgieva M (2019) Development and validation of RP-HPLC method for stability evaluation of model hydrazone, containing a pyrrole ring. Pharmacia 66(3): 127-134. https://doi.org/10.3897/pharmacia.66.e47035
Figure 7 Chromatograms indicating the behavior of D_5d in the presence of buffer with pH 2.0 and at 37°C at 0th min (A) and at 30th min (B).
Figure 4 from: Tzankova D, Peikova L, Vladimirova S, Georgieva M (2019) Development and validation of RP-HPLC method for stability evaluation of model hydrazone, containing a pyrrole ring. Pharmacia 66(3): 127-134. https://doi.org/10.3897/pharmacia.66.e47035
Figure 4 Chromatogram of standard solution of the aldehyde d (tR = 1.283) as possible degradation product.
Figure 3 from: Tzankova D, Peikova L, Vladimirova S, Georgieva M (2019) Development and validation of RP-HPLC method for stability evaluation of model hydrazone, containing a pyrrole ring. Pharmacia 66(3): 127-134. https://doi.org/10.3897/pharmacia.66.e47035
Figure 3 Chromatogram of standard solution of the hydrazide D-5 (tR = 4.380) as possible degradation product.
Figure 8 from: Tzankova D, Peikova L, Vladimirova S, Georgieva M (2019) Development and validation of RP-HPLC method for stability evaluation of model hydrazone, containing a pyrrole ring. Pharmacia 66(3): 127-134. https://doi.org/10.3897/pharmacia.66.e47035
Figure 8 Chromatograms indicating the behavior of D_5d in the presence of buffer with pH 9.0 and at 37°C at 0th min (A) and at 210th min (B).
Figure 9 from: Tzankova D, Peikova L, Vladimirova S, Georgieva M (2019) Development and validation of RP-HPLC method for stability evaluation of model hydrazone, containing a pyrrole ring. Pharmacia 66(3): 127-134. https://doi.org/10.3897/pharmacia.66.e47035
Figure 9 Chromatograms indicating the behavior of D_5d in the presence of buffer with pH 13.0 and at 37°C at 0th min (A) and at 30th min (B).
Data from: A probabilistic metric for the validation of computational models
A new validation metric is proposed that combines the use of a threshold based on the uncertainty in the measurement data with a normalised relative error, and that is robust in the presence of large variations in the data. The outcome from the metric is the probability that a model's predictions are representative of the real world based on the specific conditions and confidence level pertaining to the experiment from which the measurements were acquired. Relative error metrics are traditionally designed for use with series of data values but orthogonal decomposition has been employed to reduce the dimensionality of data matrices to feature vectors so that the metric can be applied to fields of data. Three previously published case studies are employed to demonstrate the efficacy of this quantitative approach to the validation process in the discipline of structural analysis, for which historical data was available; however, the concept could be applied to a wide range of disciplines and sectors where modelling and simulation plays a pivotal role.
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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.