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Skip to Search Results- 29Greiner, Russell (Computing Science)
- 3Bolduc, Francois (Pediatrics)
- 2Schuurmans, Dale (Computing Science)
- 1Brown, Matthew (Psychiatry)
- 1Brown, Matthew R.G. (Psychiatry)
- 1Bulitko, Vadim (Computing Science)
- 9Machine Learning
- 8Machine learning
- 2Artificial Intelligence
- 2Diagnosis
- 2Fmri
- 2Functional magnetic resonance imaging
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Fall 2009
This thesis addresses the challenge of prognosis, in terms of survival prediction, for patients with Glioblastoma Multiforme brain tumors. Glioblastoma is the most malignant brain tumor, which has a median survival time of no more than a year. Accurate assessment of prognostic factors is critical...
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Fall 2013
Many learning situations involve learning the conditional distribution $p(y|x)$ when the training data is drawn from the training distribution $p{tr}(x)$, even though it will later be used to predict for instances drawn from a different test distribution $p{te}(x)$. Most current approaches focus...
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Spring 2017
Co-embedding is the process of mapping elements from multiple sets into a common latent space, which can be exploited to infer element-wise associations by considering the geometric proximity of their embeddings. Such an approach underlies the state of the art for link prediction, relation...
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Spring 2017
Survival prediction is becoming a crucial part of treatment planning for most terminally ill patients. Many believe that genomic data will enable us to better estimate survival of these patients, which will lead to better, more personalized treatment options and patient care. As standard...
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Fall 2015
Researchers conduct association studies to discover biomarkers in order to gain new biological insight on complex diseases and phenotypes. Although most researchers have intuitions about what defines a biomarker and how to assess the results of an association study, there is neither a formal...
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The challenge of applying machine learning techniques to diagnose schizophrenia using multi-site fMRI data
DownloadSpring 2017
One of the main challenges for the use of machine learning techniques in neuroimaging data is the small n, large p problem. Datasets usually contain only a few hundreds of instances (n), each of which is described using hundreds of thousands of features (p). In this dissertation, we explore the...
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Spring 2018
Patients with Type I Diabetes (T1D) must take insulin injections to prevent the serious long term effects of hyperglycemia â high blood glucose (BG). These patients must also be careful not to inject too much insulin because this could induce hypoglycemia (low BG), which can be fatal. Patients...
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Fall 2022
Medical Fake News is a pervasive part of the information that people consume on the internet. It may lead people to take actions which may put the lives of their family and community in danger - such actions include vaccine hesitancy, administering unverified and harmful treatments, etc. First...
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Fall 2014
Fragile X Syndrome (FXS) is the most common genetic cause of Intellectual Disability, affecting 1 in 4000 boys and 1 in 6000 girls. Work in our lab using Drosophila melanogaster and since replicated in the mouse model has shown that excess protein synthesis is a major mechanism connecting the...
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Spring 2021
This dissertation demonstrates how to utilize data collected previously from different sources to facilitate learning and inference for a target task. Learning from scratch for a target task or environment can be expensive and time-consuming. To address this problem, we make three contributions...