Data-driven Patient Subtyping for Parkinson's Disease

Identifying novel PD subtypes using an interpretable decision-tree-based method that is robustly reproducible in an independent PD cohort.

Method workflow for training the decision tree-based PD classification model and obtaining the final PD subtypes

Motivation

Parkinson‘s Disease (PD) is a complex neurodegenerative disorder with high heterogeneity in clinical symptoms (motor and non-motor), progression course, treatment response, and genetic factors. Patient subtyping helps improve disease mechanism understanding and facilitates targeted interventions or treatment regimes.

Current Situation

Most PD subtypes are based on motor symptoms and do not focus on non-motor symptoms. General phenotype-based approaches do not provide a personalized way, and approaches considering phenotype and genotype data together are not well-explored.

Solution and Vision

We aim to develop data-driven patient subtyping methods that integrate both motor and non-motor characteristics of PD and jointly utilize clinical and genotype data. These automatically learned subtypes would be examined to identify potential markers for neurodegenerative diseases like PD.

With the help of these predictive markers, early therapeutic intervention in neurodegenerative diseases could be realized. We are working closely with Prof. Dr. Helge Frieling of the Department of Psychiatry, Social Psychiatry and Psychotherapy (MHH) and other biomedical partners at MHH associated with the Leibniz AI Lab.

Here, we will initially focus on young-onset PD patients and patients with comorbidities like schizophrenia and severe depression. Our final goal will be to develop personalized AI-based solutions to assist doctors with their day-to-day clinical practice.