Suicidal ideation (SI) fluctuates rapidly within individuals, but standard ecological momentary assessment (EMA) uses fixed schedules that miss critical risk windows. We present ARES-SI (Adaptive Reinforcement-Enhanced Sampling for Suicidal Ideation), a reinforcement learning system that personalizes EMA timing to individual risk patterns. ARES-SI combines random forest models to predict active SI probability and non-response likelihood with a Q-learning algorithm to determine optimal assessment intervals (30 min for high-risk states, 3 h for low-risk states, and next day to reduce burden).
We trained the system using EMA data from 98 participants across three studies with varied sampling frequencies (4–15 assessments per day; 2359 positive active SI responses) over 28-day protocols. In validation testing (N = 34; 374 positive active SI responses), the random forest models achieved an AUC of 0.865 for active SI and 0.750 for non-response. ARES-SI demonstrated 65% higher action selection accuracy than random scheduling (0.781 vs. 0.474; macro F1 = 0.650 vs. 0.377), with stronger detection of high-risk windows (30-min recall = 0.810 vs. 0.392) and efficient low-risk scheduling (3-h precision = 0.972 vs. 0.876).
These findings provide initial evidence that reinforcement learning can adapt assessment timing to momentary risk. We gratefully acknowledge the Center for Research Computing at the University of Notre Dame for developing and maintaining the ecological momentary assessment delivery system used in this study. This work was supported by the National Institute of Mental Health (R21MH131978).
Center for Healthy Minds, University of Wisconsin – Madison, Madison, WI, USA Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA Department of Psychology, University of Notre Dame, Notre Dame, IN, USA Department of Psychology, University of Wisconsin – Madison, Madison, WI, USA The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.
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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Jacobucci, R., Burke, T.A., Cheng, Y. et al. ARES-SI: adaptive reinforcement-enhanced sampling for suicidal ideation. npj Mental Health Res (2026). https://doi.org/10.1038/s44184-026-00251-0 DOI: https://doi.org/10.1038/s44184-026-00251-0
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