Electrical & Computer Engineering, Department of
Unobtrusive Data Collection in Clinical Settings for Advanced Patient Monitoring and Machine Learning
Date of this Version
Walker Arce. (2023) Unobtrusive Data Collection in Clinical Settings for Advanced Patient Monitoring and Machine Learning. Master's Thesis. University of Nebraska-Lincoln.
When applying machine learning to clinical practice, a major hurdle that will be encountered is the lack of available data. While the data collected in clinical therapies is suitable for the types of analysis that are needed to measure and track clinical outcomes, it may not be suitable for other types of analysis. For instance, video data may have poor alignment with behavioral data, making it impossible to extract the videos frames that directly correlate with the observed behavior. Alternatively, clinicians may be exploring new data modalities, such as physiological signal collection, to research methods of improving clinical outcomes that are incompatible with their existing tools. Both problems warrant the exploration of improving the tools available to clinicians and developing them in a way that allows future customization for future research and clinical needs.
This thesis covers the development and user perceived usability of a custom software tool, called cometrics, that was designed to address this data gap and be accessible enough to cover future use cases. Various use cases of this software are explored and a survey from existing users was conducted to provide comparison to existing tools. Additionally, the free and open-source nature of the software not only ensures confidence in the handling of private health information, but also allows anyone to inspect and modify the source code for their specific needs.
After deploying this software tool to two independent clinical spaces within the Munroe-Meyer Institute at the University of Nebraska Medical Center, two novel datasets were collected. A physiological dataset focused on expressions of emotion in children was analyzed using statistical models and benchmark machine learning techniques. Secondly, a video-based dataset focused on the expression of severe behavior in children was used for detecting instances of hitting. Both datasets demonstrate the rudimentary capability to train machine learning models to automate annotation of clinical data for both efficiency and early warning use cases. These advances are accelerated by the intersection of clinical practice and engineering, which is made easier using a tool that is made for both parties.
Advisers: Benjamin Riggan and James Gehringer
A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfilment of Requirements For the Degree of Master of Science, Major: Electrical Engineering, Under the Supervision of Professors Benjamin Riggan and James Gehringer. Lincoln, Nebraska: May, 2023
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