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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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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 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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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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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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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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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...
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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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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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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-...