Systematic Reconstruction of Disease Networks from Longitudinal Blood Data for Causal Discovery and Intervention Analysis

TL;DR

This paper presents a framework that converts longitudinal blood test data into disease networks for causal discovery, enabling interpretable and clinically actionable insights for healthcare decision support.

Contribution

It introduces a systematic pipeline to transform clinical time series data into causal disease networks, advancing interpretability and testability in medical AI applications.

Findings

Reconstructed 105 disease patterns from blood data.

Demonstrated causal network inference from longitudinal data.

Provided a pathway for explainable AI in clinical diagnosis.

Abstract

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.