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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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Spring 2020
Mapping the macrostructural connectivity of the living human brain is one of the primary goals of neuroscientists who study connectomics. The reconstruction of a brain's structural connectivity, aka its connectome, typically involves applying expert analysis to diffusion-weighted magnetic...
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Spring 2011
Standard survival analysis focuses on population-based studies. The objective of our work, survival prediction, is different: to find the most accurate model for predicting the survival times for each individual patient. We view this as a regression problem, where we try to map the features for...
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Learning Individual Readmission-Free Survival Distributions using Longitudinal Medical Events
DownloadFall 2023
The rate of 30-day hospital readmission is a common measurement of hospital quality, which can affect the funding a hospital receives. Over a quarter of readmissions are estimated to be preventable with adequate interventions, but these interventions are themselves costly. For this reason, many...
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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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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 2015
Graphical models use the intuitive and well-studied methods of graph theory to implicitly represent dependencies between variables in large systems. They can model the global behaviour of a complex system by specifying only local factors.This thesis studies inference in discrete graphical models...
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Spring 2014
Each patient with Type-1 diabetes must decide how much insulin to inject before each meal to maintain an acceptable level of blood glucose. The actual injection dose is based on a formula that takes current blood glucose level and the meal size into consideration. While following this insulin...
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Spring 2013
Many machine learning algorithms learn from the data by capturing certain interesting characteristics. Decision trees are used in many classification tasks. In some circumstances, we only want to consider fixed-depth trees. Unfortunately, finding the optimal depth-d decision tree can require time...
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Fall 2009
Understanding biochemical reactions inside cells of individual organisms is a key factor for improving our biological knowledge. Signaling pathways provide a road map for a wide range of these chemical reactions that convert one signal or stimulus into another. In general, each signaling pathway...