P.0083 More alike than different: the striking similarity of healthy and depressive individuals across nine modalities

Generalizability of Clinical Prediction Models in Mental Health - Real-World Validation of Machine Learning Models for Depressive Symptom Prediction

“…To the best of our knowledge, this study includes the most extensive independent validation in the field of mental health research to date. In contrast to previous studies (10,16), we show robust generalization performance across nine independent sites comprising over 2,600 participants, reflecting the full spectrum of heterogeneity and diversity present in real-world patient populations. This suggests that real-world validation of mental health symptom prediction models is possible, despite substantial sample heterogeneity.…”

“…While previous research in study populations shows that predictive models which include more than one data modality, such as clinical, neuroimaging, and genetic data, achieve better performance (24) we demonstrate that symptom severity prediction is possible with sparse features that can be collected during the clinical routine. This is in line with previous findings on the particular importance of clinical information when predicting symptom trajectories and treatment outcome in mental health research (16,17). The extracted features, encompassing two personality dimensions, somatic symptom severity, childhood emotional abuse, and global functioning, and thus a mixture of state and trait variables, consistently form a predictive pattern for depression severity across diverse patient populations, irrespective of illness stage or treatment setting.…”

“…Although imaging and genetic data have proven to be invaluable for advancing precision medicine outside of mental health (12)(13)(14)(15), previous mental health research has repeatedly demonstrated the particular relevance of training models on clinical information when predicting symptom trajectories and treatment outcome in disorders such as schizophrenia or depression (16,17). However, despite the technical feasibility of implementing structured collection of clinical information, the widespread absence of harmonized machine-readable clinical data persists across research and clinical settings, primarily due to a lack of uniform data standards and shared ontologies in mental health.…”

Accurate Machine Learning Prediction in Psychiatry Needs the Right Kind of Information

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An increasing emphasis is being placed on the potential for machine learning (ML) approaches 1 to aid in classificationandtreatmentselectioninpsychopathology.The benefits of ML models for precision medicine applications are clear, as they can aid in identifying individualized interventions and risk profiles. However, without access to enough information in a given data set to accurately predict outcomes at the individual level, 2 ML models will be unable to reach their full potential.Given the inadequate performance of ML models in psychiatric contexts to date, 3,4 it is important to consider whether ML models have sufficient information to generate accurate predictions. Herein, we discuss the importance of ample information for model performance and contrast prediction in psychiatry with successes in other fields.

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“…Given the inadequate performance of ML models in psychiatric contexts to date, 3,4 it is important to consider whether ML models have sufficient information to generate accurate predictions. Herein, we discuss the importance of ample information for model performance and contrast prediction in psychiatry with successes in other fields.…”