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datasets available to search
ShareScore release 0.9.0
Dataset results
96 results for “Machine learning algorithms”
Systematic Machine Learning Algorithm for Rapid Thrombosis Detection
ClinicalTrials.gov study NCT06842446. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
TPF Machine Learning Algorithms
ClinicalTrials.gov study NCT04983316. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking [2]
GEO Series GSE261275. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Database and Images of Cutaneous Leishmaniasis Direct Smears: Detection of Leishmania spp. Parasite in Microscopy Images Using Machine Learning Algorithms.
<p>This database and images were created for Cutaneous Leishmaniasis research, mainly for computer vision and machine learning studies. The dataset is organized into two main folders, designed to facilitate access and analysis of the images and corresponding labeling data.</p> <p>The first folder, 'EPVIEW_LC_S,' contains a total of 500 PNG images, each with a resolution of 2592 x 1944 pixels. These images represent positive direct smears with Cutaneous Leishmaniasis. The organization of the images follows a hierarchical structure, distributed across 50 folders (L1 – L50), where each folder symbolizes an individual exam slide. Inside each main folder, there are two subfolders that house the images of the two samples present on each slide, with 5 images per subfolder, corresponding to the 5 microscopic fields captured for each sample. This results in a set of 10 images per slide.</p> <p>The second folder, 'Etiquetado_LC_T,' contains the labeling process carried out for each image with the collaboration of two experts in the field. A total of 7,905 <em>Leishmania spp.</em> parasites were identified and labeled. Using bounding boxes, the precise dimensions of the parasites were recorded, including width, height, and the central location of each parasite within the image, with (x, y) coordinates, along with the parasite label. This information was stored in JSON files, whose names correspond directly to the images they reference, ensuring an accurate relationship that facilitates the use of the data for research purposes. The organization structure of the labeling folder mirrors that of the image folder.</p>
A Clinical Trial to Evaluate the Efficacy of the Morley Medical Sepsis (MMS) Software Device in Predicting Sepsis in Adult Patients Using Artificial Intelligence (AI) Machine Learning Algorithms
ClinicalTrials.gov study NCT04606862. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Machine Learning and Artificial Intelligence Algorithms to Optimize the Performance and Delivery of Acute Dialysis
ClinicalTrials.gov study NCT07312929. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Application of Machine Learning Algorithms to Identify Optimal Candidates for Primary Tumor Resection in Patients with Metastatic Non-small Cell Neuroendocrine Tumors
ClinicalTrials.gov study NCT06621147. IPD Sharing: Not stated. Countries: 0. Publications: 0.
In This Study, the Sponsor Would Like to Collaborate with Institution and Investigator to Aggregate Participants Data and to Pilot Its Software Algorithm Using Machine Learning and Threshold Based Met
ClinicalTrials.gov study NCT06798688. IPD Sharing: NO. Countries: 0. Publications: 0.
Study on the Performance of a Machine Learning Algorithm Recognizing and Triaging Large Vessel Occlusions Using Non-contrast CT Scans
ClinicalTrials.gov study NCT06216457. IPD Sharing: NO. Countries: 0. Publications: 0.
A Machine Learning Algorithm to Predict Health Clinical Situations in Primary Healthcare for Frail Older Adults.
ClinicalTrials.gov study NCT06013709. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Dataset related to article "An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study "
<p>This record contains raw data related to article “An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study"</p> <p>Abstract:</p> <p><strong>Introduction: </strong> Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning.</p> <p><strong>Material and methods: </strong> We conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation.</p> <p><strong>Results: </strong> 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality.</p> <p><strong>Conclusions: </strong> Machine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.</p>
Constructing a rat model of stress cardiomyopathy and screening for diagnostic markers using machine learning algorithms
GEO Series GSE223385. Rattus norvegicus. 20 samples. Type: Expression profiling by high throughput sequencing.
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm
<p>Using the uploaded code in preparation of the Flood vulnerability maps.</p><p> </p>
Supplementary Data and Models of Melt-based Thermo-barometer for paper "'No Free Lunch' in Tabular Geochemical Data: An example of Shallow versus Deep Machine Learning Algorithms for Geothermobarometry"
Open the record for dataset details and reuse information.
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm.
<p>The used data in the paper</p>
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm.
<p>Flood points and Meteorological data</p>
ScienceDex guides
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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.