Exploration of machine learning techniques to examine the journey to neuroendocrine tumor diagnosis with real-world data.

Aim: Machine learning reveals pathways to neuroendocrine tumor (NET) diagnosis. Patients & methods: Patients with NET and age-/gender-matched non-NET controls were retrospectively selected from MarketScan claims. Predictors (e.g., procedures, symptoms, conditions for which NET is misdiagnosed) were examined during a 5-year pre-period to understand presence of and time to NET diagnosis using conditional inference trees. Results: Among 3460 patients with NET, 70% had a prior misdiagnosis. 10,370 controls were included. Decision trees revealed combinations of factors associated with a high probability of being a patient with NET (e.g., abdominal pain, an endoscopic/biopsy procedure, vomiting) or longer times to diagnosis (e.g., asthma diagnosis with visits to >6 providers). Conclusion: Decision trees provided a unique examination of the journey to NET diagnosis.