Forecasting alcohol lapse risk up to two weeks in advance using time-lagged machine learning models
- 1Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- 2Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America
Abstract
Alcohol use disorder is a chronic, relapsing condition. Individuals must monitor risk and take proactive steps to manage and reduce it indefinitely to prevent lapse. Smartphone sensing and machine learning offer promise as scalable tools for automating risk monitoring and delivering personalized support during recovery. Research has demonstrated that machine learning models using ecological momentary assessment can generate highly accurate predictions of immediate lapse risk (e.g., within the next hour or day). However, many risks require supports that are not immediately available. These supports may need to be planned or involve coordination with others. In such cases, individuals may benefit from advance warning about changes in their lapse risk and the contributing factors. To meet this need, we developed machine learning models to predict future alcohol lapses within 24-hour prediction windows, lagged by 1 day, 3 days, 1 week, and 2 weeks from the prediction timepoint. We engineered features from 4x daily ecological momentary assessments from individuals (N = 151; 51% male; mean age = 41; 87% non-Hispanic White) in early recovery (≤ 8 weeks of abstinence) from alcohol use disorder over a three-month period. We trained and evaluated models using nested cross-validation. Median posterior auROC values were high (0.85–0.89) across models, though performance decreased modestly with longer lag time. Models performed worse for non-advantaged groups (non-White and/or Hispanic, income below federal poverty line, female) compared to advantaged groups (non-Hispanic White, income above federal poverty line, male). Past alcohol use, abstinence self-efficacy, and craving were the most important features, with the magnitude of their importance varying meaningfully by lag time. These findings demonstrate the feasibility of predicting alcohol lapses up to two weeks in advance. Embedding these models within a recovery monitoring and support system could enable adaptive, personalized care with enough warning to implement recovery supports not immediately available. Improving model fairness and optimizing the delivery of model feedback to sustain engagement remain critical next steps.
Related
- A data-driven approach to hepatitis C forecasting using machine learning and epidemiological models
- Forecasting the moisture content of live forest fuels with a transformer machine learning model and Sentinel-2 satellite data
- A Context-Aware Framework for Biological Health Index Harmonization and Machine Learning-Aided Health Forecasting of Waterbodies
- Stock-level volatility forecasting using machine learning: A comparative study of GARCH, Random Forest, LSTM, and GRU on US Equities
- Learning and forecasting shared evolutionary pathways to multi-drug resistance across global pathogens
BibTeX
@article{wyant2026forecasting,
title = {{Forecasting alcohol lapse risk up to two weeks in advance using time-lagged machine learning models}},
author = {Kendra Wyant and Gaylen E. Fronk and Jiachen Yu and Claire E. Punturieri and John J. Curtin},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0356893},
url = {https://doi.org/10.1371/journal.pone.0356893},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Forecasting alcohol lapse risk up to two weeks in advance using time-lagged machine learning models
AU - Kendra Wyant
AU - Gaylen E. Fronk
AU - Jiachen Yu
AU - Claire E. Punturieri
AU - John J. Curtin
PY - 2026
DA - 2026/09/08
JO - PLOS One
DO - 10.1371/journal.pone.0356893
UR - https://doi.org/10.1371/journal.pone.0356893
AB - Alcohol use disorder is a chronic, relapsing condition. Individuals must monitor risk and take proactive steps to manage and reduce it indefinitely to prevent lapse. Smartphone sensing and machine learning offer promise as scalable tools for automating risk monitoring and delivering personalized support during recovery. Research has demonstrated that machine learning models using ecological momentary assessment can generate highly accurate predictions of immediate lapse risk (e.g., within the next hour or day). However, many risks require supports that are not immediately available. These supports may need to be planned or involve coordination with others. In such cases, individuals may benefit from advance warning about changes in their lapse risk and the contributing factors. To meet this need, we developed machine learning models to predict future alcohol lapses within 24-hour prediction windows, lagged by 1 day, 3 days, 1 week, and 2 weeks from the prediction timepoint. We engineered features from 4x daily ecological momentary assessments from individuals (N = 151; 51% male; mean age = 41; 87% non-Hispanic White) in early recovery (≤ 8 weeks of abstinence) from alcohol use disorder over a three-month period. We trained and evaluated models using nested cross-validation. Median posterior auROC values were high (0.85–0.89) across models, though performance decreased modestly with longer lag time. Models performed worse for non-advantaged groups (non-White and/or Hispanic, income below federal poverty line, female) compared to advantaged groups (non-Hispanic White, income above federal poverty line, male). Past alcohol use, abstinence self-efficacy, and craving were the most important features, with the magnitude of their importance varying meaningfully by lag time. These findings demonstrate the feasibility of predicting alcohol lapses up to two weeks in advance. Embedding these models within a recovery monitoring and support system could enable adaptive, personalized care with enough warning to implement recovery supports not immediately available. Improving model fairness and optimizing the delivery of model feedback to sustain engagement remain critical next steps.
SN - 1932-6203
ER -