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

Lay abstract We present the novel analytic approach of machine learning using real-world data to describe patient pathways to neuroendocrine tumor (NET) diagnosis. Due to the rarity and presentation of the disease, NET diagnosis is commonly inaccurate and delayed. We aimed to demonstrate the potential of analytics using conditional inference trees. Decision trees revealed specific combinations of characteristics 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). Results from this study support prior literature and add advanced analyses that take initial steps toward developing tools aimed to help clinicians with early and accurate NET diagnosis. The methodology can be improved upon and translated to other diseases.