Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 33 for “"data imputation"”.
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NBA Sleep Tracking Data Imputation
This thesis investigates imputation methods for nights of missing sleep wearable data from NBA Academy athletes. Sparsity in sleep tracking data arises as a result of behavioral non-compliance or device malfunction, hindering the NBA Academy's ability to provide actionable insights that improve …
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Novel Techniques for Single-cell RNA Sequencing Data Imputation and Clustering
… of the major challenges in analyzing scRNA-seq data is the prevalence of dropouts, which are instances where gene expression is not detected despite being present in the cell. Dropouts occur due to technical limitations and can introduce excessive noise into the data, obscuring the true …
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Missing data imputation in a clinical registry with deep generative models
Missing data is a common problem in all data driven algorithms. An incomplete dataset can bring bias to the trained model, or cause failures in the deployment of models that require a complete input. A clinical registry is a record of patients information about their health history, status and the …
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Topics in Bayesian adaptive clinical trial design using dynamic linear models and missing data imputation in logistic regression.
… and, thus, it is not applicable for continuous data. The traditional design in Phase II determines if a new drug will be further tested in Phase III based on only drug efficacy and allocates an equal number of patients to each dosage, ignoring dose efficacy. Because of the limitations of …
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Partial least squares structural equation modelling with incomplete data. An investigation of the impact of imputation methods.
Despite considerable advances in missing data imputation methods over the last three decades, the problem of missing data remains largely unsolved. Many techniques have emerged in the literature as candidate solutions. These techniques can be categorised into two classes: statistical methods of …
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Deep learning for grouped data
… applying deep learning algorithms to groups of data. My claim is that groups should be represented as random variables whose values should be inferred from data. This approach has the potential to unlock solutions in many important domains of machine learning, including disentangling the …
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Machine learning for problems with missing and uncertain data with applications to personalized medicine
… real-world applications, we frequently encounter data which include missing and uncertain values. This thesis explores the problem of learning from missing and uncertain data with a focus on applications in personalized medicine. In the first chapter, we present a framework for classification when …
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Autoencoding variational inference for the visualization of velocity-enriched scRNA-seq data
… used to visualize complex expression profiling data. The embedding of expression data is typically based solely on expression levels, which can yield inaccuracies in the representation of the lower-dimensional data. By augmenting scRNA-seq data with velocities for each cell, we can develop …
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A control system viewpoint for manipulating heat equation and maximum entropy principle for design of experiments
… entropy-based approach for optimum feature data imputation and selection of experiments. Tactical selection of experiments to estimate an underlying model is an innate task across various fields. Since each experiment has costs associated with it, selecting statistically significant …
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Health-AIM: An artificial intelligence approach for inference with clinical health datasets
… artificial intelligence (AI) models need to use data to make reliable decisions regarding patient trajectories and treatments. Incorrect decisions can lead to strain on both caregiver and patient, and clinical datasets often have low volume, high dimensionality, and many missing values. This …
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Addressing Deficiencies from Missing Data in Electronic Health Records
… health records (EHRs) contain a wealth of data that can be used to improve patient-centered outcomes. In particular, EHRs have been used for disease prediction, data-driven clinical decision support, patient trajectory modeling, etc. However, it is common that EHRs data contain substantial …
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Power Grid Partitioning and Monitoring Methods for Improving Resilience
… monitoring using Phasor Measurement Unit (PMU) data. Strategies for data imputation and prediction exploiting the spatio-temporal correlation in PMU measurements are outlined. A deep-learning-based methodology for identifying the location of temporary power systems faults is also illustrated. As …
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Data Augmentation and Machine Learning for Risk Assessment in Healthcare Associated Infections
Acquisition and analysis of extensive datasets is, today, a central tool in most research fields. Machine learning provides powerful methods to obtain descriptive and predictive models for the data in many applications. The acquisition of quality information is fundamental for the reliability and …
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Novel Machine Learning Algorithms for Personalized Medicine and Insurance
… personalized care. As an increasing amount of data is being collected, computational performance is improved, and new algorithms are developed, machine learning has been viewed as the key analytical tool that will advance healthcare delivery. Nevertheless, until recently, despite the enthusiasm …
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New algorithms in machine learning with applications in personalized medicine
… in areas such as health care where abundant data are increasingly being collected. However, imperfections in the data pose a major challenge to realizing their full potential: missing values, noisy observations, and unobserved counterfactuals all impact the performance of data-driven methods. …
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Advances in Bayesian Machine Learning: From Uncertainty to Decision Making
… approximate inference to “big model $\times$ big data” regimes, many open challenges remain. For instance, how to properly quantify the parameter uncertainties for complicated, non-identifiable models (such as neural networks)? How to properly handle the uncertainties caused by missing data, and …
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Retail Price Time Series Imputation
… regular, discrete, retail price time series datasets acquired through crowdsourcing. Crowdsourcing is a means of data collection whereby independent individuals push publicly-available information to an information consolidator who then distributes it back to the individuals for their …
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Addressing Missing Data and Scalable Optimization for Data-driven Decision Making
Data-driven decision making has become a necessary commodity in virtually every domain of human endeavor, fueled by the exponential growth in the availability of data and the rapid increase in our computing power. In principle, if the collected data contain sufficient information, it is possible to …
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Herbal medicines:physician's recommendation and clinical evaluation of St.John's Wort for depression
… of the included trials, applied methods of data imputation and transformation for incomplete trial data and examined sources of heterogeneity in the design and results of those trials. Thirty randomised controlled trials, which were heterogeneous in design, were identified. Our meta-analysis …
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Statistical Modeling and Analysis of Bivariate Spatial-Temporal Data with the Application to Stream Temperature Study
… for analyzing bivariate spatial-temporal data in a stream temperature study. In the first part, I focus our analysis on the individual stream. A time varying coefficient model (VCM) is used to study the relationship between air temperature and water temperature for each stream. The time …
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