University of Illinois at Chicago
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Deep Learning and Adversarial Approach to Time Series Classification and Healthcare Engineering

thesis
posted on 2023-05-01, 00:00 authored by Samuel Harford
This dissertation explores the applications of deep learning techniques in time series classification and healthcare, particularly in the context of out-of-hospital cardiac arrest (OHCA). Deep learning algorithms have shown their ability to identify underlying patterns in both fields. The contributions of this dissertation include the development of a multivariate adversarial generator, the use of octave convolutions to improve classification accuracy, a transfer learning algorithm for detecting out-of-distribution data, and a deep learning technique to model OHCA survival and decision-making. These contributions aim to support OHCA community interventions and showcase the potential of deep learning in healthcare. With the availability of large datasets and new sensing technologies, deep learning techniques are rapidly evolving and hold great promise in fields beyond classification tasks.

History

Advisor

Darabi, Houshang

Chair

Darabi, Houshang

Department

Mechanical and Industrial Engineering

Degree Grantor

University of Illinois at Chicago

Degree Level

  • Doctoral

Degree name

PhD, Doctor of Philosophy

Committee Member

Li, Lin Anahideh, Hadis Del Rios, Marina Karim, Fazle

Submitted date

May 2023

Thesis type

application/pdf

Language

  • en

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