Systematic Reconstruction of Disease Networks from Longitudinal Blood Data for Causal Discovery and Intervention Analysis
We explore the hyperparameters and introduce a methodological framework to convert disease patterns from time series data of blood test results into correlation graphs for causal hypothesis exploration. The networks represent hypotheses that can then be validated or rejected both for causal discovery and causal analysis (under intervention). We synthetically recreated a repository of 105 typical disease longitudinal patterns extracted from medical guidance and research literature of common blood markers to build a systematic pipeline to translate multidimensional clinical data into intervenable disease networks for causal discovery and causal analysis. This study demonstrates that knowledge graphical models reconstructed from longitudinal data can transform routine medical data into clinically interpretable structures. By integrating multiple thresholding strategies and causal graph design, the framework has the purpose to move beyond statistical correlation toward clinically and testable inference networks. These results highlight a practical pathway for more transparent, explainable, and scalable tools in clinical decision support for AI training, precision healthcare and predictive medicine, offering interpretable, clinically actionable outputs that support safer use of AI in differential diagnosis.