Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning Approach


Purpose: Severe and febrile neutropenia present serious hazards to cancer patients undergoing chemotherapy. We seek to develop a machine learning-based neutropenia prediction model that can be used to assess risk at the initiation of a chemotherapy cycle. Materials and Methods: We leverage rich Electronic Medical Records data from a large healthcare system and apply machine learning methods to predict severe and febrile neutropenic events. We outline the data curation process and challenges posed by Electronic Medical Records data. We explore a range of algorithms with an emphasis on model interpretability and ease-of-use in a clinical setting. Results: Our final proposed model demonstrates an out-of-sample AUC of 0.865 (95% CI 0.830-0.891) in the prediction of neutropenic events based on only 20 clinical features. The model validates known risk factors and offers insight into potential novel clinical indicators and treatment characteristics that elevate risk. It relies on factors that are directly extractable from Electronic Medical Records, providing a tool can be easily integrated into existing workflows. A cost-based analysis provides insight into optimal risk thresholds and offers a framework for tailoring algorithms to individual hospital needs. Conclusion: A better understanding of neutropenic risk on an individual level enables a more informed approach to patient monitoring and treatment decisions.

JCO Clinical Cancer Informatics