Predicting Chemotherapy-associated Thrombocytopenia In Real World Clinical Settings

Chemotherapy-induced thrombocytopenia is a frequent challenge in the management of cancer patients and can limit the ability to maintain effective dosing and treatment duration. In this study, we assess real-world rates of chemotherapy-associated thrombocytopenia, measure the impact on patient dosing, and explore the potential of machine learning methods, using commonly available clinical variables, to predict development of this common, potentially treatment-limiting side effect.