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 24 for “"survival prediction"”.
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Survival Prediction For Brain Tumor Patients Using Gene Expression Data
… of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports …
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MRI Radiomics Modeling and Survival Prediction of Pancreatic Ductal Adenocarcinoma Patients
… characterized by its late detection and 5-year survival of less than 10% for both men and women as of 2022. While many cancers come with symptoms of varying degrees, pancreatic cancer is largely asymptomatic until the disease reaches its later stages. A variety of factors, both clinical and …
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Signal Processing for Early Warning Arrhythmia Detection and Survival Prediction for Clinical Decision
According to the British Heart Foundation, UK, there is a population of around 7 million living in the UK with heart and circulatory diseases; about 25% of all the deaths in the UK are caused by cardiovascular diseases and more than 30,000 people a year suffer cardiac arrest out-of-hospital. As …
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Comparative Analysis of Discrimination and Calibration Accuracy of Discrete Survival, Random Forests, and Neural Networks in Health-Related Survival Prediction Models
Prediction models for survival analysis are commonly used in biomedical sciences to understand the onset of certain diseases. Traditional statistical models have been employed for the previous years, however, their limitations and inability to handle big data sets has made a way for the …
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Deep representation learning for cancer transcriptomics: towards robust models for heterogeneous data
… causal regularisation approach for cancer survival prediction. This integrates observational patient data with interventional CRISPR-interference experiments to enhance survival prediction robustness. We find that deep learning holds great promise for addressing distributional shifts in …
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Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis
… biological processes associated to patients' survival time at the cellular and molecular level is critical not only for developing new treatments for patients but also for accurate survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause …
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Survival Model and Estimation for Lung Cancer Patients.
… Cox proportional hazard regression for the survival study of a group of lung cancer patients. The covariates in the hazard function are estimated by maximum likelihood estimation following the proportional hazards regression analysis. Although the proportional hazards model does not give an …
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Comprehensive Analysis of Lung Cancer Prognostic Factors
… features, upon which an image-based survival prediction model was built and independently validated for lung adenocarcinoma. Second, in patient level, a nomogram was built with demographic and clinical variables for patients with small cell lung cancer. The nomogram was implemented …
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Predicting the risk and trajectory of intensive care patients using survival models
… This research focuses on predicting the survival of ICU patients throughout their stay. Unlike traditional static mortality models, this survival prediction is explored as an indicator of patient state and trajectory. Using survival analysis techniques and machine learning, models are …
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Physically Motivated Feature Development for Machine Learning Applications
… certain genes are both predictive of patient survival and correlated with tumor shape. We develop features that summarize tumor shapes and therefore serve as surrogates for the genetic content of tumors, allowing survival prediction. Our final analysis and the main focus of this document is …
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Study of prognostic markers in advanced cancer
… informal and subjective. Clinicians base survival predictions upon clinical experience, clinical intuition and knowledge of cancer trajectories. Prognostic factors have been identified and validated in patients with cancer. These can be clinical markers or biomarkers. Clinical markers …
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Reframing Cox Proportional Hazards Model for Big Data and Neural Networks
… this dissertation is to propose frameworks for survival analysis and prediction with survival data that include many observations, ultra-high dimensional features, or images. We propose frameworks that are computationally efficient and stable and are amenable to stochastic-based optimization …
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Reverse-Engineering of Genetic Regulatory Pathways in Human Cancer
… distinct. A conditional inference tree-based survival prediction model is built from the combination of clinical information and the membership of MetaChips. It is shown that prediction of patient‘s early relapse is improved by incorporating these molecular-based tumour subclasses, compared …
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Development of a prognostic and predictive tool for chemotherapy response in patients with locally advanced and metastatic pancreatic adenocarcinoma
… and the limited availability of reliable survival prediction tools continue to hinder the implementation of truly personalized care. This doctoral thesis aimed to address several of these critical gaps by analyzing a real-world cohort of patients treated for PDAC at the San Pedro …
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Artificial Intelligence for System Medicine: Methods and Applications
… scheduling, which integrates an ML-based survival prediction model and a stochastic optimization formulation that balances screening delay and screening frequency. Finally, we apply predictive and prescriptive analytic methods to improve general medical outcomes in Part 3 and Part 4, …
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Advancing Radiotherapy Treatment Through Artificial Intelligence-Driven Approaches
… and organs-at-risk, a lack of tools for early prediction of treatment outcomes, and the potential radiation toxicity and side effects. Fortunately, ultra-high dose rates (FLASH) irradiation emerges as a promising new modality of radiotherapy that has the potential to reduce the radiation …
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Enhancing Neural Network Performance through SHAP-based Latent Class Integration
… in a real-world application involving survival prediction in colorectal cancer patients using data from The Cancer Genome Atlas (TCGA). A set of routinely collected clinical features were used (e.g., age, stage, histology, race) which resulted in JEDI-net identifying clinically …
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Novel Network-Based Models for High-dimensional Data
… improve biomarker discovery and clinical outcome prediction. However, most existing statistical methods fail to incorporate such network infor mation. To address this limitation, we developed a high-dimensional generalized linear model (HDnetGLM) that explicitly integrates network structures …
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Gene Selection and Cancer Classification Using a Multidimensional Fuzzy Deep Learning Approach for Gene Expression Data
… commonly employed for developing cancer prediction models using associated gene expression and mutation data. This thesis provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (i.e Breast, Lung, Kidney, Liver, …
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Computational imaging and multiomic biomarkers for precision medicine: characterizing heterogeneity in lung cancer.
… enhance the ability to predict progression-free survival in a preliminary cohort of patients with stage 4 NSCLC, treated with first-line anti-PD1/PDL1 checkpoint inhibitor therapy PEMBROLIZUMAB. This study also showed that mitigation of the heterogeneity introduced by voxel spacing and image …
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