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Limited and heterogeneous data
The available data comes from different sources and structures. Standardization rules and synthetic scenario generation were used to create a more consistent analytical foundation.
Rational Medicine Optimization System
A data-driven research project focused on medication waste, prescription planning, healthcare risk analysis and decision support.
Year
2026
Status
Research Project
Role
Data Analysis, Statistical Modelling, Machine Learning
Duration
TÜBİTAK 2209-A Project
Context and objective
The project explores how heterogeneous healthcare and municipal data can be transformed into decision-support insights for rational medicine production and prescription planning. It combines data preparation, statistical analysis, machine learning and scenario-based simulation.
Challenges and solutions
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Method selection
Different questions required different methods. Classification, association analysis, regression and non-parametric testing were combined instead of forcing every problem into one model.
Key features
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Heterogeneous data cleaning and standardization
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Synthetic data generation with Latin Hypercube Sampling
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CART-based decision analysis
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Apriori association-rule mining
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Beta regression modelling
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Dunn post-hoc statistical testing
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Risk stratification and scenario analysis
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Reproducible Docker-based research environment
Tools used in the project
PythonPandasScikit-learnSciPyDockerGit
Selected screens and system views
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