University of Pennsylvania

Timothy D Solberg

Gilmer Valdes

Lyle H Ungar

Eric Eaton

Ryan Scheuermann

Arthur Olszanski

Jose Marcio Luna

C Y Hung

Marc Bellerive

Charles B Simone, Ii

A system and method for generating a decision tree having a plurality of nodes, arranged hierarchically as parent nodes and child nodes, comprising: generating a node including: receiving i) training data including data instances, each data instance having a plurality of attributes and a corresponding label, ii) instance weightings, iii) a valid domain for each attribute generated, and iv) an accumulated weighted sum of predictions for a branch of the decision tree; and associating one of a plurality of binary prediction of an attribute with each node including selecting the one of the plurality of binary predictions having a least amount of error; in accordance with a determination that the node includes child nodes, repeat the generating the node step for the child nodes; and in accordance with a determination that the node is a terminal node, associating the terminal node with an outcome classifier; and displaying the decision tree including the plurality of nodes arranged hierarchically.

Systems and methods for generating improved decision trees

A method comprises receiving one or more plan parameters of a first radiation treatment plan for a first patient and one or more passing rate data for the first treatment plan; generating a predictive model for passing rate data from the plan parameters of the first radiation treatment plan and the passing rate data for the first treatment plan; receiving one or more plan parameters of a second radiation treatment plan for a second patient; and applying the predictive model to the plan parameters of the second radiation treatment to generate one or more predicted passing rate data for the plan parameters for the second patient.

System and method for virtual radiation therapy quality assurance

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Philadelphia, PA, United States of America

Timothy D Solberg