In a collaboration with a leading global pharmaceutical company, Volv Global applied machine learning to 24 million UK primary care records, surfacing approximately 1,200 likely-undiagnosed GEP-NET patients – and finding they are 5–7 years younger than those currently diagnosed. The model achieved a ROC-AUC of 0.756 when discriminating GEP-NETs from clinically similar mimic conditions. GEP-NETs are rare malignancies whose non-specific symptoms mean patients typically wait nearly five years before receiving a confirmed diagnosis; five-year survival rates for high-grade disease (G3) may be as low as 25%. Volv Global applied its proprietary machine learning methodology through the inTrigue framework to the Optimum Patient Care Research Database (OPCRD), covering approximately 24 million de-identified records from around 1,100 UK GP practices. A positive cohort of 1,857 GEP-NET patients was constructed using a procedure that recovers patients not captured by direct code queries. The negative cohort was drawn from clinically relevant comparator conditions.