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29
datasets available to search
ShareScore release 0.7.1
Dataset results
29 results for “Automated Machine Learning”
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>
devCellPy: A machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data
GEO Series GSE184943. Homo sapiens; Mus musculus. 1186 samples. Type: Expression profiling by high throughput sequencing.
devCellPy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data
<p>A major informatic challenge in single cell RNA-sequencing analysis is the precise annotation of datasets where cells exhibit complex multilayered identities or transitory states. Here, we present <em>devCellPy</em> a highly accurate and precise machine learning-enabled tool that enables automated prediction of cell types across complex annotation hierarchies. To demonstrate the power of <em>devCellPy</em>, we construct a murine cardiac developmental atlas from published datasets encompassing 104,199 cells from E6.5-E16.5 and train <em>devCellPy</em> to generate a cardiac prediction algorithm. Using this algorithm, we observe a high prediction accuracy (>90%) across multiple layers of annotation and across de novo murine developmental data. Furthermore, we conduct a cross-species prediction of cardiomyocyte subtypes from in vitro<em>-</em>derived human induced pluripotent stem cells and unexpectedly uncover a predominance of left ventricular (LV) identity that we confirmed by an LV-specific TBX5 lineage tracing system. Together, our results show devCellPy to be a useful tool for automated cell prediction across complex cellular hierarchies, species, and experimental systems.</p>
Automated ICD Coding of Primary Diagnosis Based on Machine Learning
ClinicalTrials.gov study NCT04817423. IPD Sharing: NO. Countries: 1. Publications: 0.
Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers
GEO Series GSE124203. Homo sapiens. 1745 samples. Type: Expression profiling by array.
Exploring Context-Free Languages via Planning: The Case for Automating Machine Learning -- Additional Plots
<p>A comparison to SOTA AutoML approach on various datasets</p>
Raw data used to build models in SIMON Automated Machine Learning
<p>Here you can find all data and all information regarding each generated dataset.<br> For each dataset there are 4 files:</p> <p>json_info : This file contains, number of features with their names and number of subjects that are available for the same dataset<br> data_testing: data frame with data used to test trained model<br> data_training: data frame with data used to train models<br> results: direct unfiltered data from database</p> <p><br> Files are written in feather format.</p> <p><a href="https://gist.github.com/LogIN-/00d7628e0850f843ba84a678fac0a103">Here is an example</a> of data structure for each file in repository</p> <p> </p>
Automated Generation of Complex Bugs in the Machine Learning Era
<p>Artifacts for reproducing BugFarm.</p>
Automated Generation of Complex Bugs in the Machine Learning Era
<p>Datasets and artifacts.</p>
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.