Harnessing Small‐Data Machine Learning for Transformative Mental Health Forecasting: Towards Precision Psychiatry With Personalised Digital Phenotyping

ABSTRACT Advances in digital phenotyping have opened the door to continuous, individualised monitoring of mental health, but realising the full potential of these data demands machine learning models that can operate effectively in ‘small‐data’ regimes—where per‐user data are sparse, irregular and noisy. This article explores the feasibility, challenges and opportunities of small‐data machine learning approaches for forecasting individual‐level mental health trajectories. We examine the limitations of traditional clinical tools and population‐level models and argue that fine‐grained time‐series forecasting, powered by models such as tabular prior‐data fitted networks (TabPFN), Gaussian processes, Kalman filters and meta‐learning strategies, offers a path towards personalised, proactive psychiatry. Emphasis is placed on key clinical requirements: real‐time adaptation, uncertainty quantification, feature‐level interpretability and respect for interindividual variability. We discuss implementation barriers including data quality, model transparency and ethical considerations and propose practical pathways for deployment—such as integrated biosensor platforms and just‐in‐time adaptive interventions (JITAIs). We highlight the emerging convergence of small‐data ML, mobile sensing and clinical insight as a transformative force in mental healthcare. With interdisciplinary collaboration and prospective validation, these technologies have the potential to shift psychiatry from reactive symptom management to anticipatory, personalised intervention.