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- 2Schuurmans, Dale (Computing Science)
- 1Bolduc, Francois (Pediatrics)
- 1Brown, Matthew (Psychiatry)
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- 1Bulitko, Vadim (Computing Science)
- 2Wen, Junfeng
- 1Allen, Felicity R
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- 8Machine learning
- 7Machine Learning
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- 2Schizophrenia
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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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Addressing the Challenges of Applying Machine Learning for Predicting Mental Disorders and Their Prognosis Using Two Case Studies
DownloadSpring 2019
Ghoreishiamiri, Seyedehreyhaneh
One of the principal applications of machine learning in psychiatry is to build automated tools that can help clinicians predict the diagnosis and prognosis of mental disorders using available data from patients’ profiles. Here, in two different studies, we investigate ways to use machine learn-...
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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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Assessing the Feasibility of Learning Biomedical Phenotype Patterns Using High-Throughput Omics Profiles
DownloadSpring 2014
A decade after the completion of the human genome project, the rapid advancement of the high-throughput measurement technologies has made omics (genomics, epigenomics, transcriptomics, metabolomics) profiling feasible. The availability of such omics profiles has raised the hope for the...
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Fall 2009
In this thesis we consider the task of catching a moving target with multiple pursuers, also known as the “Pursuit Game”, in which coordination among the pursuers is critical. Our testbed is inspired by the pursuit problem in video games, which require fast planning to guarantee fluid frame...
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Machine learning for medical applications with limited data: Incorporating domain expertise and addressing domain-shift
DownloadFall 2022
Machine learning has the potential to help medical experts to deliver better healthcare. There are, however, important technical challenges that need to be solved before we can develop reliable models for clinical practice, including: (1) Limited number of labeled instances, (2) Uncertainty of...
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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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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 2020
While it is very difficult to diagnose/prognosis psychiatric disorders reliably, especially in early course, such early diagnosis/prognosis is critical for producing an effective treatment. This necessity has motivated many researchers to apply machine learning approaches to high-dimensional...
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Using Survival Prediction Techniques to Learn Consumer-Specific Reservation Price Distributions
DownloadSpring 2015
A consumer's "reservation price" (RP) is the highest price that s/he is willing to pay for one unit of a specified product or service. It is an essential concept in many applications, e.g., personalized pricing, auction and negotiation. While consumers will not volunteer their RPs, we may be able...