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285 results for “Machine learning dataset”
Dataset para Estimação de Torque de Motores de Combustão Interna Flex-Fuel Utilizando Machine Learning
<p>Dataset foi projetado para auxiliar na modelagem e estimação do torque de motores de combustão interna do tipo Flex Fuel utilizando técnicas de Machine Learning. </p>
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>
Dataset for "Classification of Stream, Hyperconcentrated, and Debris Flow Using Dimensional Analysis and Machine Learning"
<p>Du J. et al., (2022). Dataset for "Classification of Stream, Hyperconcentrated, and Debris Flow Using Dimensional Analysis and Machine Learning", Water Resources Research</p> <p>Table S1: Hydrologic Paramteres and Dimensionless Numbers of Debris Flows</p> <p>Table S2: Hydrologic Paramteres and Dimensionless Numbers of Hyperconcentrated Flows</p> <p>Table S3: Hydrologic Paramteres and Dimensionless Numbers of Stream Flows</p> <p>Table S4: Hydrologic Paramteres and Dimensionless Numbers of Lahars</p>
Dataset related to article "Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process"
<p> This record contains raw data related to article “Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process"</p> <p><em>Purpose:</em> Patient-specific quality assurance (PSQA) is performed to ensure that modulated treatment plans can be delivered as intended, but constitutes a substantial workload that could slow down the radiotherapy process and delay the start of clinical treatments. In this study, we investigated a machine learning (ML) tree-based ensemble model to predict the gamma passing rate (GPR) for volumetric modulated arc therapy (VMAT) plans.</p> <p><em>Materials and Methods:</em> 5622 VMAT plans from multiple treatment sites were selected from a database of Institution 1 and the ML model trained using 19 metrics. PSQA analyses were performed automatically using criteria 3%/1 mm (global normalization, absolute dose, 10% threshold) and 95% action limit. Model’s performance was evaluated on an out-of-sample test set of Institution 1 and on two independent sets of measurements collected at Institution 2 and Institution 3. Mean absolute error (MAE), as well as the model’s sensitivity and specificity, were computed.</p> <p><em>Results:</em> The model obtained a MAE of 2.33%, 2.54% and 3.91% for the three Institutions, with a specificity of 0.90, 0.90 and 0.68, and a sensitivity of 0.61, 0.25, and 0.55, respectively. Small positive median values of the residuals (i.e., the difference between measurements and predictions) were observed for each Institution (0.95%, 1.66%, and 3.42%). Thus, the model’s predictions were, on average, close to the real values and provided a conservative estimation of the GPR.</p> <p><em>Conclusions</em>: ML models can be integrated into clinical practice to streamline the radiotherapy workflow, but they should be center-specific or thoroughly verified within centers before clinical use.</p> <p> </p>
Dataset related to article: "Machine learning to predict mortality after rehabilitation among patients with severe stroke"
<p>We provide the raw data used for the following article:</p> <ul> <li>Scrutinio D, Ricciardi C, Donisi L, Losavio E, Battista P, Guida P, Cesarelli M, Pagano G, D'Addio G.<br> <em>Machine learning to predict mortality after rehabilitation among patients with severe stroke.</em> "Sci Rep." 2020 Nov 18;10(1):20127.<br> doi: 10.1038/s41598-020-77243-3. PMID: 33208913; PMCID: PMC7674405.</li> </ul> <p> </p> <p><strong>Abstract:</strong> Stroke is among the leading causes of death and disability worldwide. Approximately 20–25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decision-making. Machine learning (ML) methods have gained increasing popularity in the setting of biomedical research. The aim of this study was twofold: assessing the performance of ML tree-based algorithms for predicting three-year mortality model in 1207 stroke patients with severe disability who completed rehabilitation and comparing the performance of ML algorithms to that of a standard logistic regression. The logistic regression model achieved an area under the Receiver Operating Characteristics curve (AUC) of 0.745 and was well calibrated. At the optimal risk threshold, the model had an accuracy of 75.7%, a positive predictive value (PPV) of 33.9%, and a negative predictive value (NPV) of 91.0%. The ML algorithm outperformed the logistic regression model through the implementation of synthetic minority oversampling technique and the Random Forests, achieving an AUC of 0.928 and an accuracy of 86.3%. The PPV was 84.6% and the NPV 87.5%. This study introduced a step forward in the creation of standardisable tools for predicting health outcomes in individuals affected by stroke.</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.