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Skip to Search Results- 29Greiner, Russell (Computing Science)
- 3Bolduc, Francois (Pediatrics)
- 3Brown, Matthew (Psychiatry)
- 2Greenshaw, Andrew (Psychiatry)
- 2Schuurmans, Dale (Computing Science)
- 1Brown, Matthew R.G. (Psychiatry)
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Spring 2010
This thesis presents Pathway Informed Analysis (PIA), a classification method for predicting disease states (diagnosis) from metabolic profile measurements that incorporates biological knowledge in the form of metabolic pathways. A metabolic pathway describes a set of chemical reactions that...
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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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Advancing ECG Analysis through Machine Learning: A Study on Data Generation for ECG Classification and Feature Selection For Individual Survival Prediction
DownloadSpring 2024
Electrocardiograms (ECGs) are a valuable and easily-collected measurement of heart health, reflecting its morphology (R peak, QRS duration,..) and rhythm(sequence of multiple heartbeats). With the advance of machine learning, many studies utilize electrocardiogram (ECG) signals for various...
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Spring 2019
Forouzandehmoghadam, Amirhosein
A biomarker is a feature (e.g., gene expression, SNP, etc.) that is significantly different between two classes of instances – typically case and control. Knowing these biomarkers can help us understand a biological condition or identify the appropriate treatment for a certain disease. Many...
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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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Building an expert-system based conversational agent to provide personalised resources about neurological disorders
DownloadSpring 2022
Researchers developing artificially intelligent conversational agents (aka, chat- bots) seek effective ways to provide personal assistance to users with various needs. We have implemented a web-based conversational agent that recom- mends resources to help clients (caregivers of patients...
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Spring 2016
One of the key obstacles to the effective use of mass spectrometry (MS) in high throughput metabolomics is the difficulty in interpreting measured spectra to accurately and efficiently identify metabolites. Traditional methods for automated metabolite identification compare the target MS spectrum...
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Fall 2023
A survival dataset describes a collection of instances, such as patients, and associates each instance with either the time until an event (such as death), or the censoring time (eg, when the instance is lost to follow-up), which is a lower bound on the time until the event. While there are...
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Fall 2022
As one of the main tasks in studying causality, the goal of Causal Inference is to determine "whether" (and perhaps "how much") the value of a certain variable (i.e., the effect) would change, had another specified variable (i.e., the cause) changed its value. A prominent example is the...
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Fall 2012
Functional Magnetic Resonance Imaging (fMRI) measures the dynamic activity of each voxel of a brain. This dissertation addresses the challenge of learning a diagnostic classifier that uses a subject’s fMRI data to distinguish subjects with neuropsychiatric disorders from healthy controls. fMRI...