Evonik
Data Mining for More Efficient Clinical Studies
Industry: Manufacturing & Industry
Evonik is a global leader in specialty chemicals, operating in over 100 countries.
The company does not produce end products but delivers innovative solutions that improve the performance of products like tires or the effectiveness of pharmaceuticals.
In clinical research, Evonik relies on data-driven processes to gain patient-specific insights and develop more efficient and sustainable treatment approaches.
Status quo
Evonik aimed to make clinical data analysis more efficient by quickly identifying patterns and correlations.
Traditional study processes were largely manual and based on generic patient models. There was no data-driven approach to grouping patients with similar characteristics or deriving precise therapy recommendations.
The challenge
The challenge was to transform large, heterogeneous clinical datasets into concrete, operational insights.
Variables like age, body size, fat changes, gender, mobility, and activity levels needed to be processed in a way that forms valid patient clusters. At the same time, data like pain perception, sleep behavior, and other clinical metrics had to be integrated. The goal was to build a model that forms the foundation for faster studies, personalized therapies, and improved treatment outcomes.
The solution
CONVOTIS implemented a data mining platform that analyzes clinical data using clustering, a-priori analysis, and predictive models.
The solution processes VAS data along with variables like physical measurements, mobility, and pain indicators. Data cleansing is performed using Moving Average, Wavelet, and Kalman filters. Algorithms detect recurring patterns, generate itemsets, and derive association rules.
The results directly inform study planning and therapy development — improving efficiency and research quality.
The result
The solution enables shorter study durations, deeper insights into patient profiles, and more accurate segmentation.
Evonik benefits from more efficient clinical studies, sustainable treatment approaches, and significantly improved patient care quality. Data-driven analysis forms the foundation for informed clinical decisions and personalized treatment protocols.
Technical
Highlights
Even complex projects can be summarised in clear technical terms. Key figures and distinctive features provide insight into the concrete implementation – measurable, tangible, and transparent.
- Analysis of heterogeneous clinical data (VAS, Womac, sleep behavior, pain perception)
- Clustering and a-priori methods for pattern recognition
- Data cleansing with Moving Average, Wavelet, and Kalman filters
- Development of predictive models for therapy planning and study optimization
- Extraction of itemsets, association rules, and patient profiles
Data mining has taken clinical research at Evonik to a new level. Intelligent pattern recognition, precise patient segmentation, and data-driven analytics make studies more efficient, therapies more targeted, and treatment outcomes more sustainable.
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