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 223 for “"Predictive Performance"”.
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Evaluating the predictive performance of cytotoxic T lymphocyte epitope prediction tools using Elispot assay data
… 3.2, IEDB_ANN, IEDB_ARB Matrix and IEDB_SMM. The predictive performances of all four tools individually and collectively was statistically assessed using non-parametric Spearman rank-order correlation tests. It was found that none of the four tested tools yielded binding affinity predictions that …
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Assessing predictive performance and transferability of species distribution models for freshwater fish in the United States
… that there was no significant difference in the performance of the three modeling techniques. Spatial transferability could be improved by using spatial logistic regression under Lasso regularization in the training of SDMs and by matching the range and location of predictor variables between …
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Improving Lung Cancer Risk Prediction: Integration of Novel Predictors and Modelling Using Machine Learning Random Forest versus the Validated PLCOm2012 Logistic Regression Model
… to predict 6-year LC risk and assessed using predictive performance measures including discrimination and calibration. Results of the current study indicated a superior predictive performance of the PLCOm2012 LR model compared to the risk model developed using RF, with area under the …
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Scalable Multi-label Classification
… classification: methods which exhibit high predictive performance, but are also able to scale up to larger problems. The first major contribution is the pruned sets method, which is able to model label correlations directly for high predictive performance, but reduces overfitting and …
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Bayesian Optimisation of Hyperparameters in Regression Models for Smart Energy and Environmental Systems
… optimisation significantly improves predictive performance and computational efficiency. For example, optimisation of artificial neural network models improved prediction accuracy for smart home energy consumption from 60\% to approximately 85\%, representing an improvement of around …
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Switching State Space Modeling via Constrained Inference for Clinical Outcome Prediction
… models such as LSTMs have demonstrated strong predictive performance on multivariate clinical time series, they often lack interpretability. To address this gap, this thesis proposes a framework that combines the predictive strength of neural networks with the interpretability of latent …
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Application of dynamic prediction models for longitudinal biomarkers and clinical outcomes in low and middle-income settings
… to the assumption of their universally improved predictive performance over traditional approaches such as the Cox proportional hazards-based prediction model. In addition to applying an extension of existing models to correctly model semicontinuous biomarker data (two-part joint model), this …
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TRANSLATIONAL GENOMICS: THE IMPACT OF GENETIC RISK SCORE ON PATIENTS AND PHYSICIANS
… of these markers are used in combination, the predictive performance is strong; however, the clinical utility of multi-marker tests is not known. This thesis addresses a unifying question: Can genomic test results safely and effectively target healthcare decisions for patients and physicians?
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Graph Attention Mechanisms for Modeling Pathway-Level Importance from Gene Expression
… to improve robustness, interpretability, and predictive performance. This work presents a pathway-aware variational autoencoder which integrates curated gene-pathway structure into latent representation learning. This includes the integration of gene interactions through graph neural networks, …
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Preventing Opioid Overdose: From Prediction to Operationalization
… Prior work has focused on the statistical performance of such models without considering operational implications. Predicting the most severe outcome (fatal overdose) is a particular challenge due to imbalanced datasets. We partner with Staten Island Performing Provider System to access …
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Approaches to developing clinically useful Bayesian risk prediction models
… accuracy, in some cases surpassing the performance of clinicians. However, evidence is lacking that deployment of these models has improved care and patient outcomes. That is, their clinical usefulness is debatable. One barrier to demonstrating such improvement is the basis used to …
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The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression
… Each decision model yielded significantly high predictive performance. However, models predicting need of treatment across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models representing the overall patient population (ROC of 78.87%). Next, we assessed the value of adding SDH …
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Prediction of Social Events Using a Classifier System
… criteria, strength types that derive from the performance dimensions of frequency, accuracy, uniqueness, activity, and base rate. Furthermore, several aggregation techniques are proposed to resolve conflicts across replications, and several similarity coefficients and distance metrics are …
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Gaussian processes for state space models and change point detection
… methods. These methodologies are evaluated on predictive performance on six real world data sets, which include three environmental data sets, one financial, one biological, and one from industrial well drilling. Gaussian processes are capable of generalizing standard linear time series models. …
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AutoFE : efficient and robust automated feature engineering
… set of features that significantly improves the performance of any traditional classification using an evolutionary algorithm. We demonstrate the effectiveness and robustness of our approach by conducting an extensive evaluation on 8 datasets and 5 different classification algorithms. We show …
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Random forests and their application to heteroscedastic drug design data
… this problem, providing an explanation for the performance of random forests and exploring the practical implications of this explanation. We explore an analogy between random forest and a Bayesian procedure of model selection and use this to explain the different behaviours of a random forest. …
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Multimodal AI for Hospital Readmission Prediction Among Older Adults
… home care. The aim is to create a robust predictive framework that leverages comprehensive patient data collected during hospitalisation to enhance risk assessment and clinical decision-making. By integrating multiple data sources, this approach improves predictive accuracy and provides …
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Understanding travel mode choice: A new approach for city scale simulation
… This approach is used to compare the relative predictive performance of a complete suite of Machine Learning (ML) classification algorithms, as well as traditional utility-based choice models. Furthermore, a new assisted specification approach, where a fitted ML classifier is used to inform the …
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Integrating Epigenetic Priors For Improving Computational Identification of Transcription Factor Binding Sites
… positive rate. In an attempt to improve the predictive performance of a PWM, we use a Hidden Markov Model to incorporate chromatin structure, in particular histone modifications. The HMM captures physical interactions between distinct HMs. Indeed, the integration of sequence based PWM models …
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