To evaluate whether reported alcohol consumption scenarios are consistent with observed biomarker patterns using a physiological digital twin model, addressing challenges in health care and forensic settings.
Approach:
Key Findings:
The model predicts biomarker profiles that align with observed data in controlled scenarios.
Combining multiple biomarkers improves differentiation between similar drinking patterns.
The model's predictions are influenced by input assumptions, leading to broader prediction ranges, which may limit its reliability in real-world applications.
Interpretation:
The digital twin model offers a novel approach to reconstructing alcohol intake scenarios, but its current validation limits its generalizability and reliability in real-world applications, necessitating further research.
Limitations:
Limited validation against a single independent drinking scenario restricts generalizability across diverse real-world patterns.
Does not fully account for interindividual variability in biomarker dynamics, which may affect accuracy.
Observed discrepancies in biomarker elimination patterns indicate incomplete physiological representation, impacting model reliability.
Conclusion:
While the model shows promise for forensic applications and future research, it is not yet validated for clinical decision-making or legal use, underscoring the need for further validation.
