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Applying a Machine Learning Approach to Predict Acute Radiation Toxicities for Head and Neck Cancer Patients

2019; Elsevier BV; Volume: 105; Issue: 1 Linguagem: Inglês

10.1016/j.ijrobp.2019.06.520

1879-355X

Jay P. Reddy, William D. Lindsay, Christopher G. Berlind, Christopher Ahern, Andrew Holmes, Benjamin D. Smith, Jack Phan, Steven J. Frank, G. Brandon Gunn, David I. Rosenthal, William H. Morrison, Adam S. Garden, Gregory M. Chronowski, Shalin Shah, Lauren L. Mayo, Clifton D. Fuller,

Head and Neck Cancer Studies

We hypothesized that employing a machine learning approach could permit accurate prediction of unplanned hospitalizations, feeding tube placement, and significant weight loss experienced by head and neck (HN) cancer patients secondary to radiation therapy (RT). To test this, we merged data from an internal web-based charting tool (known as Brocade), the electronic health record (Epic), and the record/verify system to develop predictive models of these toxicities.