The tool was developed using the electronic health records (EHRs) of the spectrum of all cancer patients treated at a tertiary cancer center and novel machine learning algorithms constructed by the coauthors. Delivered as a web or mobile based application, it estimates the probability of mortality for a particular patient and a particular envisioned cancer treatment. This tool has the following characteristics:

1. Personalized and specific:

   The tool takes as inputs the EHR of a particular patient, the particular cancer type and a particular envisioned cancer treatment and outputs the probability of mortality adjusted for these patient characteristics.

2. Interpretable and clinically meaningful:

   Because the structure of the prediction is based on decision trees, a physician or even a patient can easily understand the reasoning behind the algorithm. The model also identifies key predictors of mortality such as change in weight.

3. Evidence based and data driven:

   The tool was informed by EHRs of more than 23,000 patients at a large national cancer hospital. We included 401 predictors including demographics, medical and treatment history, laboratory tests, and genomic results.

4. Actionable:

   The clinician can compare different envisioned treatments for a particular cancer patient with respect to the probability of mortality and make decisions that are informed by these estimates.

5. Validated and accurate:

   We compare the out-of-sample accuracy and the area under the curve (AUC) in unseen patient data from 2012-2014, with very encouraging results compared to competing approaches.

6. Based on state-of-the-art machine learning:

   The methodology of this paper is based on two novel algorithms developed by coauthors of the paper: a) the predictive decision tree algorithm was developed by Bertsimas and Dunn using optimization ideas, and b) the algorithm for missing data imputation was developed by Bertsimas, Pawlowski, and Zhuo.