Full metadata
Title
Analysis of Machine Learning Assisted Fatigue Identification in Radiology Readings
Description
Fatigue in radiology is a readily studied area. Machine learning concepts appliedto the identification of fatigue are also readily available. However, the intersection
between the two areas is not a relative commonality. This study looks to explore the
intersection of fatigue in radiology and machine learning concepts by analyzing temporal
trends in multivariate time series data. A novel methodological approach using support
vector machines to observe temporal trends in time-based aggregations of time series data
is proposed. The data used in the study is captured in a real-world, unconstrained
radiology setting where gaze and facial metrics are captured from radiologists performing
live image reviews. The captured data is formatted into classes whose labels represent a
window of time during the radiologist’s review. Using the labeled classes, the decision
function and accuracy of trained, linear support vector machine models are evaluated to
produce a visualization of temporal trends and critical inflection points as well as the
contribution of individual features. Consequently, the study finds valid potential
justification in the methods suggested. The study offers a prospective use of maximummargin classification to demarcate the manipulation of an abstract phenomenon such as
fatigue on temporal data. Potential applications are envisioned that could improve the
workload distribution of the medical act.
Date Created
2022
Contributors
- Hayes, Matthew (Author)
- McDaniel, Troy (Thesis advisor)
- Coza, Aurel (Committee member)
- Venkateswara, Hemanth (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
53 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.171646
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2022
Field of study: Computer Science
System Created
- 2022-12-20 06:19:18
System Modified
- 2022-12-20 06:19:18
- 1 year 11 months ago
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