Fast, smart, and adaptive: using machine learning to optimize mental health assessment and monitor change over time

In mental health, accurate symptom assessment and precise measurement of patient conditions are crucial for clinical decision-making and effective treatment planning. Traditional assessment methods can be burdensome, especially for vulnerable populations, leading to decreased motivation and potentially unreliable results. Computerized Adaptive Testing (CAT) has emerged as a solution, offering efficient and personalized assessments. In particular, Machine Learning-based CAT (MT-based CATs) enables adaptive, rapid, and accurate evaluations that are more easily implementable than traditional methods. This approach bypasses typical item selection processes and the associated computational costs while avoiding the rigid assumptions of traditional CAT approaches. This study investigates the effectiveness of Machine Learning-Model Tree-based CAT (ML-MT-based CAT) in detecting changes in mental health measures collected at four time points (6-month intervals between February 2018 and December 2019). Three CATs measuring generalized anxiety, depression, and social anxiety were developed and tested on a dataset with responses from 564 participants. A cross-validation approach based on real data simulations was used. Results showed that ML-MT-based CATs produced estimates of trait levels comparable to full-length tests while reducing the number of items administered by 50% or more. In addition, ML-MT-based CATs captured changes in trait levels consistent with full-length tests, outperforming short static measures.