Predicting Real-life Drinking Scenarios through a Physiological Digital Twin Incorporating Secondary Alcohol Markers
Podeus, H.; Simonsson, C.; Jakobsson, G.; Kronstrand, R.; Nyman, E.; Lövfors, W.; Cedersund, G.
Show abstract
Alcohol consumption poses significant societal challenges, necessitating accurate tools for detecting at-risk drinking. Various biomarkers reflect alcohol intake over different timeframes. Blood alcohol concentration (BAC) and breath alcohol concentration (BrAC) are commonly used for short-term detection, particularly in forensic contexts such as driving under the influence of alcohol (DUIA) cases. Moreover, the rapid kinetics of BAC and BrAC limit their utility in determining the precise timing of intake--an essential factor in legal cases involving defenses like the hipflask argument. To address this, secondary metabolites including ethyl glucuronide (EtG), ethyl sulphate (EtS), and urine alcohol concentration (UAC) offer slower, more time-sensitive profiles. Combining these markers could enable a more accurate reconstruction of past alcohol consumption events. Traditionally, mathematical models have been used to help extract information from the dynamics of alcohol-related markers. Existing mathematical models typically focus on primary markers or single secondary markers in isolation. In this study, we present an extended mathematical model that integrates all markers; BAC, EtG, EtS, and UAC into a unified framework, expanding our previous physiological twin model. Our updated model enables personalized simulations of alcohol metabolism and intake timing, which in turn creates the fundament for enhancing forensic assessments and supporting applications where accurate temporal analysis of alcohol consumption is critical. To facilitate accessibility and practical use of this analysis, we have implemented and provided an interactive web application.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Mechanistic model for human brain metabolism and the neurovascular coupling. 92%
- PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes 91%
- Bayesian Structural Time Series for Biomedical Sensor Data: A Flexible Modeling Framework for Evaluating Interventions 91%
Similar papers in this journal
- Dynamic analysis of the individual patterns of intakes, voids, and bladder sensations reported in bladder diaries collected in the LURN study 93%
- Can alcohol consumption in Germany be reduced by alcohol screening, brief intervention and referral to treatment in primary health care? Results of a simulation study 93%
- Modelling Alcohol Consumption Patterns to Enable Policy Impact Assessment 93%
Similar papers in this journal
- Peripheral blood transcriptomic profiling indicates molecular mechanisms commonly regulated by binge-drinking and placebo-effects 91%
- Machine Learning-Based Model for Behavioral Analysis in Rodents: Application to the Forced Swim Test 91%
- Accounting for endogenous effects in decision-making with a non-linear diffusion decision model 90%
Similar papers in this journal
- Intense bitterness of molecules: machine learning for expediting drug discovery 91%
- Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data. 90%
- Topological embedding and directional feature importance in ensemble classifiers for multi-class classification 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.