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Dataset results
356 results for “Machine learning models”
Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database
<p><b>Objective:</b> The predictors of in-hospital mortality for intensive care units (ICU)-admitted HF patients remain poorly characterized.We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.</p> <p><b>Design: </b>A retrospective cohort study.</p> <p><b>Setting and Participants: </b>Data were extracted from the MIMIC-III database. Data on 1,177 heart failure patients were analysed.</p> <p><strong>Methods</strong>: Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation and validation groups. Independent risk factors for in-hospital mortality were screened using XGBoost and LASSO regression models in the derivation sample. Multivariable logistic regression analysis was used to build prediction models. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients.</p> <p><b>Results:</b> Among the 1,177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression, and GWTG-HF risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (P<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram.</p> <p><b>Conclusions</b><b>:</b> Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.</p>
A Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
<p>Data used to generate the figures in "A Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model" 2021. The zip files contains the hybrid forecasts and regridded ERA5 data used to verify the forecasts as well as the SPEEDY-LLR and ML-only runs. </p>
CalcAMP: A new machine learning model for the accurate pre-diction of antimicrobial activity of peptides
<p>Datasets used for the publication: </p> <p>CalcAMP: A new machine learning model for the accurate prediction of antimicrobial activity of peptides</p>
Quantitative Assessment of the Impact of Future Land Use Changes on Flood Risk Using Remote Sensing, Machine Learning, and a Hydraulic Model
<p> </p> <p>The RF Machine learning code </p> <p>Topological, geomorphology, geology, metrological information of the Tajan watershed.</p> <p>Land use land cover images of the Tajan watershed</p> <p>River, transportation roads, villages map </p> <p>Global damage function datasets.</p>
pywaterinfo dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>This forcings dataset is the output of the pywaterinfo (https://fluves.github.io/pywaterinfo/) read in of forcing data (rain and potential evapotranspiration).</p> <p>Code related to this dataset can be found here: https://github.com/olivierbonte/master_thesis</p>
OpenEO dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>This dataset is the output of the <a href="https://openeo.org/">OpenEO</a> processing of satellite data (SAR backscatter and LAI). </p> <p>Code related to this dataset can be found <a href="https://github.com/olivierbonte/master_thesis">here</a></p>
Mechanism of building a machine learning model and its main features
<p>This figure is a flowchart summarizing the steps required to build a machine learning model in general way.</p>
Dataset of Flow Velocity Prediction in Vegetated Alluvial Channels Comparing Empirical and State-of-the-art Hybrid Machine Learning Models
<p>We compiled 447 datasets from different sources and lab- and field-based measurements. These datasets included Einstein and Banks (1950), Fenzl (1962), Kouwen et al. (1969), Ree and Crow (1977), Murota (1984), Tsujimoto and Kitamura (1990), Tsujimoto (1991), Tsujimoto (1993), Shimizu (1994), Dunn et al. (1996), Ikeda and Kanazawa (1996), Meijer (1998), Jarvela (2002), Rowinski and Kubrak (2002), Stone and Shen (2002), Poggi et al. (2004), Carollo et al. (2005), and Murphy et al. (2007).</p>
Minimal dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>The minimal dataset needed of data which can not be retrieved from the internet by APIs in the preprocessing. Consists of shape, land use and rivers for the Zwalm catchment. </p> <p>Code related to this dataset can be found here: https://github.com/olivierbonte/master_thesis </p>
Preprocessing output for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>Outputs of the local preprocessing of the OpenEO data (see <a href="https://doi.org/10.5281/zenodo.7691342">here</a>) and pywaterinfo data (see <a href="https://doi.org/10.5281/zenodo.7689200">here</a>).</p> <p>Code related to this dataset can be found <a href="http://github.com/olivierbonte/master_thesis">here</a></p>
Inverse observation operator parameters/models for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>Both saved models and results of hyperparameter tuning are given. </p> <p>Code related to this dataset can be found <a href="http://github.com/olivierbonte/master_thesis">here</a></p> <p> </p>
Tailored machine learning models for functional RNA detection in genome-wide screens
<p>The prediction of noncoding RNA and protein coding genetic loci has received<br> considerable attention in comparative genomics aiming in particular<br> at the identification of properties of nucleotide sequences that are<br> informative of their biological role in the cell. We present here a<br> software framework for the alignment-based training, evaluation and<br> application of machine learning models with user-defined<br> parameters. Instead of focusing on the one-size-fits-all approach of<br> pervasive \is annotation pipelines, we offer a framework for the<br> structured generation and evaluation of models based on arbitrary<br> features and input data, focusing on stable and explainable results.<br> Furthermore, we showcase the usage of our software package in a<br> full-genome screen of Drosophila melanogaster and evaluate<br> our results against the well-known but much less flexible program<br> RNAz.</p> <p> </p>
Dataset and Model Files for the Paper "Concrete Type Inference for Code Optimization using Machine Learning with SMT Solving"
<p>Dataset and model files for the paper "Concrete Type Inference for Code Optimization using Machine Learning with SMT Solving".</p>
Figures for 'Global streamflow modelling using process-informed machine learning'
<p>High-quality figures for the article 'Global streamflow modelling using process-informed machine learning' (<a href="https://doi.org/10.2166/hydro.2023.217">https://doi.org/10.2166/hydro.2023.217</a>).</p>
Code for the article titled "A New Approach to Ozone Profiling Based on MAX-DOAS Observations and Stacking Machine Learning Models"
Open the record for dataset details and reuse information.
Developing a Machine Learning Model to Predict Pleural Adhesion Preoperatively Using Pleural Ultrasound
ClinicalTrials.gov study NCT06423066. IPD Sharing: NO. Countries: 1. Publications: 2.
Epigenomic and Machine Learning Models to Predict Pancreatic Cancer
ClinicalTrials.gov study NCT06334458. IPD Sharing: Not stated. Countries: 4. Publications: 0.
Predicting Postoperative Pulmonary Infection in Elderly Patients Undergoing Major Surgery: a Study Based on Logistic Regression and Machine Learning Models
ClinicalTrials.gov study NCT06491459. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Research on the Diagnostic Value of Machine Learning Model Based on Clinical Data in Patients With Coronary Heart Disease
ClinicalTrials.gov study NCT05018715. IPD Sharing: NO. Countries: 1. Publications: 1.
Risk Factors and Machine Learning Model for Aminoglycines Related Acute Kidney Injury
ClinicalTrials.gov study NCT05533593. IPD Sharing: Not stated. 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.