Early identification of persistent somatic symptoms in primary care: data-driven and theory-driven predictive modelling based on electronic medical records of Dutch general practices.

The findings indicate low to moderate diagnostic accuracy for early identification of PSS based on routine primary care data. Nonetheless, simple clinical decision rules based on structured symptom/disease or medication codes could possibly be an efficient way to support GPs in identifying patients at risk of PSS. A full data-based prediction currently appears to be hampered by inconsistent and missing registrations. Future research on predictive modelling of PSS using routine care data should focus on data enrichment or free-text mining to overcome inconsistent registrations and improve predictive accuracy.