PhD Proposal: Exploring the Power of Machine Learning in Medical Research: A Focus on Movement Disorder Diagnosis and Age-Related Hearing Loss
Rana Khalil
Monday, July 31, 2023, 10:00 am-12:00 pm 
Abstract
The medical research field is experiencing a remarkable evolution due to the application of data science and machine learning (ML) techniques and new developments in these fields. The accessibility of large datasets, enhanced computing capabilities, and advanced algorithms have opened up new possibilities for extracting valuable insights, identifying patterns, and developing predictive models from complex biomedical data. This integration has the potential to revolutionize medical research, resulting in enhanced diagnostic capabilities, personalized treatment approaches, and ultimately, improved patient care.
In this proposal, I explore the impact of data science and ML in medical research, with a specific focus on movement disorder diagnosis and age-related hearing loss. In the first of these domain areas, I focus on the use of wearable sensor data to accurately identify individuals with Parkinson's disease (PD) based on their movements during several motor tasks. I extract an extensive set of features from the sensor data and train a diverse array of classifiers. Additionally, I investigate the influence of confounding factors on classifier decisions and perform feature importance analyses to evaluate the significance of each feature and mobility task for specific models and predictions. The findings highlight the effectiveness of ML models in successfully identifying individuals with movement disorders using data from wearable sensors. Furthermore, the analysis of feature importance emphasizes the substantial contribution of a small number of complex mobility tasks involving different types of movements, suggesting the potential for streamlining data collection protocols without compromising the predictive performance of the models.
In the second domain area, I study age-related hearing loss by constructing ensemble models to examine data from participants with diverse ages and varying degrees of hearing loss. By integrating audiometric, perceptual, electrophysiological, and cognitive data, I predict speech perception in challenging auditory conditions like noise, reverberation, and time compression. Leveraging ML techniques, my objective is to identify the variables that are highly predictive of demanding speech-perception conditions, thereby confirming existing associations and potentially uncovering novel ones. The findings underscore the critical role of audiometric thresholds, particularly within the 1--4 kHz range, and emphasize the utility of composite variables spanning multiple frequencies in accurately predicting speech perception. Furthermore, basic temporal processing ability demonstrates a moderate influence, whereas cognitive factors and extended high-frequency thresholds exhibit limited to negligible predictive capability in this context. Continued research and exploration of associations will contribute to a deeper understanding of the complex interplay between speech perception, aging, hearing loss, and cognition.
In this proposal, I explore the impact of data science and ML in medical research, with a specific focus on movement disorder diagnosis and age-related hearing loss. In the first of these domain areas, I focus on the use of wearable sensor data to accurately identify individuals with Parkinson's disease (PD) based on their movements during several motor tasks. I extract an extensive set of features from the sensor data and train a diverse array of classifiers. Additionally, I investigate the influence of confounding factors on classifier decisions and perform feature importance analyses to evaluate the significance of each feature and mobility task for specific models and predictions. The findings highlight the effectiveness of ML models in successfully identifying individuals with movement disorders using data from wearable sensors. Furthermore, the analysis of feature importance emphasizes the substantial contribution of a small number of complex mobility tasks involving different types of movements, suggesting the potential for streamlining data collection protocols without compromising the predictive performance of the models.
In the second domain area, I study age-related hearing loss by constructing ensemble models to examine data from participants with diverse ages and varying degrees of hearing loss. By integrating audiometric, perceptual, electrophysiological, and cognitive data, I predict speech perception in challenging auditory conditions like noise, reverberation, and time compression. Leveraging ML techniques, my objective is to identify the variables that are highly predictive of demanding speech-perception conditions, thereby confirming existing associations and potentially uncovering novel ones. The findings underscore the critical role of audiometric thresholds, particularly within the 1--4 kHz range, and emphasize the utility of composite variables spanning multiple frequencies in accurately predicting speech perception. Furthermore, basic temporal processing ability demonstrates a moderate influence, whereas cognitive factors and extended high-frequency thresholds exhibit limited to negligible predictive capability in this context. Continued research and exploration of associations will contribute to a deeper understanding of the complex interplay between speech perception, aging, hearing loss, and cognition.
Examining Committee
Bio
Rana Khalil is a PhD student in the Department of Computer Science at the University of Maryland, College Park. She is a member of the CBCB lab working with Michael P. Cummings. She is interested in applying machine learning techniques to study different medical conditions with a focus on movement disorders and age-related hearing loss. She received her Master's degree in Computer and Systems Engineering from Alexandria University in Egypt.
This talk is organized by Tom Hurst