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

Abstract

PURPOSE Severe and febrile neutropenia present serious hazards to patients with cancer 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 (EMRs) data from a large health care system and apply machine learning methods to predict severe and febrile neutropenic events. We outline the data curation process and challenges posed by EMRs 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 area under the receiver operating characteristic curve of 0.865 (95% CI, 0.830 to 0.891) in the prediction of neutropenic events on the basis of 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 EMRs, provided 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.

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“…More recently, Wiberg et al [45] employed machine learning techniques to predict neutropenic events using electronic medical records (EMRs). Their model, emphasizing interpretability and clinical applicability, achieved a commendable out-of-sample area under the receiver operating characteristic curve of 0.865 based on 20 clinical features.…”

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“…Point-of-care electronic medical record data were used to train and validate a variety of ML models (Figure 2). Of six ML models studied, the preferred one requires only 20 clinical features; the model offers interpretability and a low data extraction burden, addressing two common barriers to adoption 16…”

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“…Patients with neutropenia are generally more susceptible to infections and sepsis (Nesher and Rolston, 2013;Kochanek et al, 2019). There are many studies which have identified risk factors for neutropenia through the use of machine learning (Cho et al, 2020;Venäläinen et al, 2021;Wiberg et al, 2021). Machine learning uses algorithms in order to uncover possible relationships between variables in a dataset.…”