Birkenbihl, Colin: Towards enabling precision medicine in Alzheimer's disease and Parkinson's disease. - Bonn, 2024. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5-74832
@phdthesis{handle:20.500.11811/11443,
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-74832,
doi: https://doi.org/10.48565/bonndoc-251,
author = {{Colin Birkenbihl}},
title = {Towards enabling precision medicine in Alzheimer's disease and Parkinson's disease},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2024,
month = mar,

note = {Alzheimer's disease and Parkinson's disease are prominent progressive neurodegenerative diseases, with a significant clinical and economic impact on patients, their families, and society as a whole. Despite numerous clinical trials over the last two decades, no disease-modifying treatment is available for either disease. The past trial failures are attributed in part to the heterogeneity of the diseases with respect to clinical presentation and pathological manifestation and the late timing of interventions in the course of the disease. The emerging healthcare paradigm of precision medicine aims to address these challenges by bringing the right drug to the right patient at the right time.
The research presented in this thesis relies on artificial intelligence and machine learning to advance precision medicine in the context of Alzheimer's disease and Parkinson's disease. We contribute to a deeper understanding of these complex diseases through patient subtyping and present novel predictive models for patient stratification. Furthermore, we make a new patient-level dataset openly accessible for research purposes and present a web application that facilitates the exploration of large cohort datasets in the Alzheimer’s domain. Additionally, we investigate systematic biases in commonly used data resources, proposing methods to assess and understand them. By doing so, we empower researchers to make informed decisions about data selection, enhancing the reliability, generalizability, and utility of their findings. Finally, we introduce a novel artificial intelligence-based approach for generating synthetic patient-level data, which can help overcoming limitations in real patient data.
In conclusion, the scientific advancements presented in this thesis collectively support robust data-driven research in the context of Alzheimer's disease and Parkinson's disease. They provide further insight into the heterogeneity of these debilitating diseases that could be leveraged to pave the way towards more effective clinical trials that are guided by the principles of precision medicine.},

url = {https://hdl.handle.net/20.500.11811/11443}
}

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