Generalizability of Clinical Prediction Models in Mental Health - Real-World Validation of Machine Learning Models for Depressive Symptom Prediction
Maike Richter,
Daniel Emden,
Ramona Leenings
et al.
Abstract:Mental health research faces the challenge of developing machine learning models for clinical decision support. Concerns about the generalizability of such models to real-world populations due to sampling effects and disparities in available data sources are rising. We examined whether harmonized, structured collection of clinical data and stringent measures against overfitting can facilitate the generalization of machine learning models for predicting depressive symptoms across diverse real-world inpatient an… Show more
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