⚡ Quick Summary
This study introduces state space modeling (SSM) as a novel approach for predicting lapse risks in patients recovering from alcohol use disorder (AUD). The findings indicate that SSMs outperform traditional machine learning classifiers in predictive accuracy, particularly with sufficient patient data.
🔍 Key Details
- 📊 Participants: 148 individuals in early recovery from AUD
- 🧩 Data Collection: Daily ecological momentary assessments (EMA) over 3 months
- ⚙️ Techniques Used: State space modeling, logistic regression, gradient-boosted machine learning classifiers
- 🏆 Performance Metric: Area under the receiver operating characteristic curve (AUROC)
🔑 Key Takeaways
- 📈 SSMs demonstrated superior predictive performance for lapse risks compared to traditional ML methods.
- 💡 Median posterior probabilities for SSMs being the best model were 0.997 for same-day lapse, 0.999 for lapse within 3 days, and 0.998 for lapse within 7 days with ≥30 days of data.
- 📉 With only 15 days of data, SSMs showed variable performance across tasks, highlighting the importance of data volume.
- 🌟 SSMs can fit personalized models to individual patient data, enhancing treatment personalization.
- 🔄 The framework can be applied to other mental health conditions beyond AUD.
- 🧠 Bridging two research areas: risk prediction and idiographic modeling.
- 📅 Study duration: 3-month observational study.
- 🔍 Future applications may include optimal treatment selection using digital therapeutic platforms.
📚 Background
Mental health conditions, such as substance use disorders, often require long-term assessment and treatment. Traditional methods of risk prediction have largely relied on population-level machine learning classifiers, which may not account for the unique trajectories of individual patients. This study explores the potential of idiographic approaches that tailor models to individual patient data, aiming to enhance the personalization of care.
🗒️ Study
Conducted with 148 participants in early recovery from AUD, this study utilized a 3-month observational design. Participants provided daily EMA data, which was used to train idiographic state space models (SSMs). The performance of these models was compared against logistic regression and gradient-boosted machine learning classifiers, focusing on their ability to predict lapses in alcohol use.
📈 Results
The results indicated that SSMs consistently outperformed traditional machine learning classifiers in predictive accuracy, particularly when provided with ≥30 days of EMA data. The median posterior probabilities for SSMs being the best model were remarkably high, suggesting a strong advantage in predicting lapse risks. In contrast, with only 15 days of data, the performance of SSMs varied significantly across different prediction tasks.
🌍 Impact and Implications
The implications of this study are profound. By demonstrating that SSMs can provide better predictive performance than traditional methods, this research paves the way for more personalized and effective treatment strategies in mental health care. The ability to tailor models to individual patient data not only enhances risk prediction but also opens avenues for optimal treatment selection, potentially transforming how clinicians approach patient care.
🔮 Conclusion
This study highlights the promising potential of state space modeling in the realm of mental health risk prediction. By moving beyond traditional machine learning approaches, SSMs offer a more individualized framework that can significantly improve patient outcomes. As we look to the future, further exploration of this methodology could lead to enhanced treatment strategies across various mental health conditions.
💬 Your comments
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Idiographic Lapse Prediction With State Space Modeling: Algorithm Development and Validation Study.
Abstract
BACKGROUND: Many mental health conditions (eg, substance use or panic disorders) involve long-term patient assessment and treatment. Growing evidence suggests that the progression and presentation of these conditions may be highly individualized. Digital sensing and predictive modeling can augment scarce clinician resources to expand and personalize patient care. We discuss techniques to process patient data into risk predictions, for instance, the lapse risk for a patient with alcohol use disorder (AUD). Of particular interest are idiographic approaches that fit personalized models to each patient.
OBJECTIVE: This study bridges 2 active research areas in mental health: risk prediction and time-series idiographic modeling. Existing work in risk prediction has focused on machine learning (ML) classifier approaches, typically trained at the population level. In contrast, psychological explanatory modeling has relied on idiographic time-series techniques. We propose state space modeling, an idiographic time-series modeling framework, as an alternative to ML classifiers for patient risk prediction.
METHODS: We used a 3-month observational study of participants (N=148) in early recovery from AUD. Using once-daily ecological momentary assessment (EMA), we trained idiographic state space models (SSMs) and compared their predictive performance to logistic regression and gradient-boosted ML classifiers. Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) for 3 prediction tasks: same-day lapse, lapse within 3 days, and lapse within 7 days. To mimic real-world use, we evaluated changes in AUROC when models were given access to increasing amounts of a participant’s EMA data (15, 30, 45, 60, and 75 days). We used Bayesian hierarchical modeling to compare SSMs to the benchmark ML techniques, specifically analyzing posterior estimates of mean model AUROC.
RESULTS: Posterior estimates strongly suggested that SSMs had the best mean AUROC performance in all 3 prediction tasks with ≥30 days of participant EMA data. With 15 days of data, results varied by task. Median posterior probabilities that SSMs had the best performance with ≥30 days of participant data for same-day lapse, lapse within 3 days, and lapse within 7 days were 0.997 (IQR 0.877-0.999), 0.999 (IQR 0.992-0.999), and 0.998 (IQR 0.955-0.999), respectively. With 15 days of data, these median posterior probabilities were 0.732, <0.001, and <0.001, respectively.
CONCLUSIONS: The study findings suggest that SSMs may be a compelling alternative to traditional ML approaches for risk prediction. SSMs support idiographic model fitting, even for rare outcomes, and can offer better predictive performance than existing ML approaches. Further, SSMs estimate a model for a patient's time-series behavior, making them ideal for stepping beyond risk prediction to frameworks for optimal treatment selection (eg, administered using a digital therapeutic platform). Although AUD was used as a case study, this SSM framework can be readily applied to risk prediction tasks for other mental health conditions.
Author: [‘Pulick E’, ‘Curtin J’, ‘Mintz Y’]
Journal: JMIR Form Res
Citation: Pulick E, et al. Idiographic Lapse Prediction With State Space Modeling: Algorithm Development and Validation Study. Idiographic Lapse Prediction With State Space Modeling: Algorithm Development and Validation Study. 2025; 9:e73265. doi: 10.2196/73265