This dissertation documents an investigation into Parkinson’s Disease utilizing machine learning and causal inference methods. I will cover a descriptive analysis of Parkinson’s Disease (PD) in a vast, high-quality database and present costs associated with Parkinson’s Disease medications. I also researched a causal inference method assessing the Carbidopa-Levodopa effect on two-year survival and a causal survival analysis on a one-to-five-year survival comparing no drug use and Carbidopa-Levodopa in Parkinson’s Disease patients.
For my classification with Parkinson’s gait, patients were monitored with a smartphone and an additional 6 Inertial Measurement Unit (IMU) sensors to collect clinical gait measures. I used classical machine learning algorithms on raw smartphone data to distinguish between ON and OFF times. With an average accuracy of 92.5%, this work demonstrates the feasibility of using smartphone data to distinguish between ON versus OFF walking and lays the groundwork for a real-world, corrective feedback system.
I also researched the causal effect of the most prevalent PD medication in terms of survival. In particular, I focused on the probability of two-year survival with PD patients taking Carbidopa-Levodopa and no drug use and assessing whether there was an effect on survival utilizing the doubly robust method. My results with the differences of causal effects showed a 0.013 positive increase taking Carbidopa-Levodopa indicating this medication had a significant positive effect on the two-year survival of PD patients.
I then furthered this study and conducted a causal survival analysis from one-to-five-year survival with two treatments, no drug use and Carbidopa-Levodopa. The results showed that Carbidopa- vii Levodopa had a significant effect on survival when the drug was prescribed within three years from first diagnosis and no drug use had a significant effect at four and five years of survival.
In the process of better using the current data, a descriptive statistical analysis was conducted. As such, I studied a vast and high-quality database Cerner Real-World Data and focused on people who were diagnosed with Parkinson’s Disease from 2016 to 2022. I researched the demographics, comorbidities, and medications of PD patients. After cleaning the database, my final cohort size was 110,037 subjects.
","abstract_html":"<p>This dissertation documents an investigation into Parkinson’s Disease utilizing machine learning and causal inference methods. I will cover a descriptive analysis of Parkinson’s Disease (PD) in a vast, high-quality database and present costs associated with Parkinson’s Disease medications. I also researched a causal inference method assessing the Carbidopa-Levodopa effect on two-year survival and a causal survival analysis on a one-to-five-year survival comparing no drug use and Carbidopa-Levodopa in Parkinson’s Disease patients.</p> <p>For my classification with Parkinson’s gait, patients were monitored with a smartphone and an additional 6 Inertial Measurement Unit (IMU) sensors to collect clinical gait measures. I used classical machine learning algorithms on raw smartphone data to distinguish between ON and OFF times. With an average accuracy of 92.5%, this work demonstrates the feasibility of using smartphone data to distinguish between ON versus OFF walking and lays the groundwork for a real-world, corrective feedback system.</p> <p>I also researched the causal effect of the most prevalent PD medication in terms of survival. In particular, I focused on the probability of two-year survival with PD patients taking Carbidopa-Levodopa and no drug use and assessing whether there was an effect on survival utilizing the doubly robust method. My results with the differences of causal effects showed a 0.013 positive increase taking Carbidopa-Levodopa indicating this medication had a significant positive effect on the two-year survival of PD patients.</p> <p>I then furthered this study and conducted a causal survival analysis from one-to-five-year survival with two treatments, no drug use and Carbidopa-Levodopa. The results showed that Carbidopa- vii Levodopa had a significant effect on survival when the drug was prescribed within three years from first diagnosis and no drug use had a significant effect at four and five years of survival.</p> <p>In the process of better using the current data, a descriptive statistical analysis was conducted. As such, I studied a vast and high-quality database Cerner Real-World Data and focused on people who were diagnosed with Parkinson’s Disease from 2016 to 2022. I researched the demographics, comorbidities, and medications of PD patients. After cleaning the database, my final cohort size was 110,037 subjects.</p>","abstract_has_math":false,"creators":["Pierce, Albert"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Dr. Cyril Rakovski","Dr. Adrian Vajiac","Dr. Sidy Danioko"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08-01T07:00:00Z","date_published":"2023-08-01T07:00:00Z","updated_at":"2026-07-24T01:38:31Z","subjects":["Causal Inference","Machine Learning","Parkinson's Disease","Doubly Robust Method","Carbidopa-Levodopa","Data Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_dissertations/36","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Cyril Rakovski","Dr. Adrian Vajiac","Dr. Sidy Danioko"]},{"key":"dc:creator","label":"Author","values":["Pierce, Albert"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-05-20T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational and Data Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Causal Inference","Machine Learning","Parkinson's Disease","Doubly Robust Method","Carbidopa-Levodopa","Data Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_dissertations/36"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation documents an investigation into Parkinson’s Disease utilizing machine learning and causal inference methods. I will cover a descriptive analysis of Parkinson’s Disease (PD) in a vast, high-quality database and present costs associated with Parkinson’s Disease medications. I also researched a causal inference method assessing the Carbidopa-Levodopa effect on two-year survival and a causal survival analysis on a one-to-five-year survival comparing no drug use and Carbidopa-Levodopa in Parkinson’s Disease patients.
For my classification with Parkinson’s gait, patients were monitored with a smartphone and an additional 6 Inertial Measurement Unit (IMU) sensors to collect clinical gait measures. I used classical machine learning algorithms on raw smartphone data to distinguish between ON and OFF times. With an average accuracy of 92.5%, this work demonstrates the feasibility of using smartphone data to distinguish between ON versus OFF walking and lays the groundwork for a real-world, corrective feedback system.
I also researched the causal effect of the most prevalent PD medication in terms of survival. In particular, I focused on the probability of two-year survival with PD patients taking Carbidopa-Levodopa and no drug use and assessing whether there was an effect on survival utilizing the doubly robust method. My results with the differences of causal effects showed a 0.013 positive increase taking Carbidopa-Levodopa indicating this medication had a significant positive effect on the two-year survival of PD patients.
I then furthered this study and conducted a causal survival analysis from one-to-five-year survival with two treatments, no drug use and Carbidopa-Levodopa. The results showed that Carbidopa- vii Levodopa had a significant effect on survival when the drug was prescribed within three years from first diagnosis and no drug use had a significant effect at four and five years of survival.
In the process of better using the current data, a descriptive statistical analysis was conducted. As such, I studied a vast and high-quality database Cerner Real-World Data and focused on people who were diagnosed with Parkinson’s Disease from 2016 to 2022. I researched the demographics, comorbidities, and medications of PD patients. After cleaning the database, my final cohort size was 110,037 subjects.
"]},{"key":"dc:source","label":"Dc Source","values":["A. Pierce, \"Causal inference and machine learning methods in Parkinson's Disease data analysis,\" Ph.D. dissertation, Chapman University, Orange, CA, 2023. https://doi.org/10.36837/chapman.000494"]},{"key":"dc:title","label":"Title","values":["Causal Inference and Machine Learning Methods in Parkinson's Disease Data Analysis"]}]}],"canonical_facts":{"dc:contributor":["Dr. Cyril Rakovski","Dr. Adrian Vajiac","Dr. Sidy Danioko"],"dc:creator":["Pierce, Albert"],"dc:date.available":["2025-05-20T07:00:00Z"],"dc:description.abstract":["This dissertation documents an investigation into Parkinson’s Disease utilizing machine learning and causal inference methods. I will cover a descriptive analysis of Parkinson’s Disease (PD) in a vast, high-quality database and present costs associated with Parkinson’s Disease medications. I also researched a causal inference method assessing the Carbidopa-Levodopa effect on two-year survival and a causal survival analysis on a one-to-five-year survival comparing no drug use and Carbidopa-Levodopa in Parkinson’s Disease patients.
For my classification with Parkinson’s gait, patients were monitored with a smartphone and an additional 6 Inertial Measurement Unit (IMU) sensors to collect clinical gait measures. I used classical machine learning algorithms on raw smartphone data to distinguish between ON and OFF times. With an average accuracy of 92.5%, this work demonstrates the feasibility of using smartphone data to distinguish between ON versus OFF walking and lays the groundwork for a real-world, corrective feedback system.
I also researched the causal effect of the most prevalent PD medication in terms of survival. In particular, I focused on the probability of two-year survival with PD patients taking Carbidopa-Levodopa and no drug use and assessing whether there was an effect on survival utilizing the doubly robust method. My results with the differences of causal effects showed a 0.013 positive increase taking Carbidopa-Levodopa indicating this medication had a significant positive effect on the two-year survival of PD patients.
I then furthered this study and conducted a causal survival analysis from one-to-five-year survival with two treatments, no drug use and Carbidopa-Levodopa. The results showed that Carbidopa- vii Levodopa had a significant effect on survival when the drug was prescribed within three years from first diagnosis and no drug use had a significant effect at four and five years of survival.
In the process of better using the current data, a descriptive statistical analysis was conducted. As such, I studied a vast and high-quality database Cerner Real-World Data and focused on people who were diagnosed with Parkinson’s Disease from 2016 to 2022. I researched the demographics, comorbidities, and medications of PD patients. After cleaning the database, my final cohort size was 110,037 subjects.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_dissertations/36"],"dc:source":["A. Pierce, \"Causal inference and machine learning methods in Parkinson's Disease data analysis,\" Ph.D. dissertation, Chapman University, Orange, CA, 2023. https://doi.org/10.36837/chapman.000494"],"dc:subject":["Causal Inference","Machine Learning","Parkinson's Disease","Doubly Robust Method","Carbidopa-Levodopa","Data Science"],"dc:title":["Causal Inference and Machine Learning Methods in Parkinson's Disease Data Analysis"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:38:31Z"}