During the in-hospital optimization of dopaminergic dosage for Parkinson’s disease, drug-induced dyskinesias emerge as a common side effect. Wrist-worn devices present a substantial opportunity for continuous movement recording and the supportive identification of these dyskinesias. To bridge the gap between dyskinesia assessment and machine learning-enabled detection, the recorded information requires meaningful data representations. This study evaluates and compares two distinct representations of sensor data: a task-dependent, semantically grounded approach and automatically extracted large-scale time-series features. Each representation was assessed on public datasets to identify the best-performing machine learning model and subsequently applied to our own collected dataset to assess generalizability. Data representations incorporating semantic knowledge demonstrated comparable or superior performance to reported works, with peak F₁ scores of 0.68. Generalization to our own dataset from clinical practice resulted in an observed F₁ score of 0.53 using both setups. These results highlight the potential of semantic movement data analysis for dyskinesia detection. Dimensionality reduction in accelerometer-based movement data positively impacts performance, and models trained with semantically obtained features avoid overfitting. Expanding cohorts with standardized neurological assessments labeled by medical experts is essential for further improvements.

This work introduces a novel approach to facilitate clinical research on secondary clinical data by integrating an LLM-based chatbot within a specialized platform called data hotel. The platform is designed to empower clinical researchers within our institution by enabling the generation of research hypotheses from secondary use patient data sources. Our focus in this work is on the deployment and functionality of the LLM-based chatbot within the data hotel ecosystem. The aim is to aid medical experts in visualizing and analyzing data sourced from the platform but also to enable the seamless storage of the generated code, enhancing the efficiency and reproducibility of the research process. This integration represents a significant advancement in leveraging LLM capabilities to enhance the utility and accessibility of clinical research platforms.