aceMethodological transitions in mental health research driven by machine learning
Machine learning is increasingly reshaping psychological and mental health research by complementing traditional theory-driven approaches with data-driven predictive modelling. This encompasses three key transitions: from hypothesis-testing to pattern discovery; from controlled experimental settings to naturalistic and multidimensional data ecosystems; and from explanatory theoretical models to prediction-informed theory-building and clinical translation. Nevertheless, this methodological reorientation remains i…
We further clarify that predictive accuracy, interpretability and causal explanation are related but distinct aims, and that machine learning should complement rather than replace psychological theory, causal reasoning and clinical judgement.
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- Peer-reviewedBJPsych Open2026-09-21
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Truvace Impact Record TRV-2026-1169, v2: “Methodological transitions in mental health research driven by machine learning.” Truvace, 2026-10-26. /record/TRV-2026-1169 (accessed on the day you copy it). sha256 0cc5d97935757f92…
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