Can the additive tree expand machine learning in medicine?

When health care providers order a test or prescribe medicine, they want to be 100 percent confident in their decision. That means being able to explain their decision and study it over, depending upon how a patient responds. As artificial intelligence’s footprint increases in medicine, that ability to check work and follow the path of a decision can become a bit muddied. That’s why the discovery of a once-hidden through-line between two popular predictive models used in artificial intelligence opens the door much wider to confidently spread machine learning further throughout health care. The discovery of the linking algorithm and the subsequent creation of the “additive tree” is now detailed in the Proceedings of the National Academy of Sciences (PNAS).

“In medicine, the cost of a wrong decision can be very high,” says one of the study’s authors, Lyle Ungar, a professor of computer and information science in the School of Engineering and Applied Science. “In other industries, for example, if a company is deciding which advertisement to show its consumers, they likely don’t need to double-check why the computer selected a given ad. But in health care, since it’s possible to harm someone with a wrong decision, it’s best to know exactly how and why a decision was made.”

The team, led by Jose Marcio Luna, a research associate in radiation oncology and member of the Computational Biomarker Imaging Group at Penn Medicine, and Gilmer Valdes, an assistant professor of Radiation Oncology at the University of California, San Francisco, uncovered an algorithm that runs from zero to one on a scale. When a predictive model is set to zero on the algorithm’s scale, its predictions are most accurate but also most difficult to decipher, similar to “gradient boosting” models. When a model is set to one, it is easier to interpret, though the predictions are less accurate, like “classification and regression trees” (CARTs). Luna and his co-authors subsequently developed their decision tree somewhere in the middle of the algorithm’s scale.

“Previously, people used CART and gradient boosting separately, as two different tools in the toolbox,” Luna says. “But the algorithm we developed shows that they both exist at the extreme ends of a spectrum. The additive tree uses that spectrum so that we get the best of both worlds: high accuracy and graphical interpretability.”

Read more at Penn Medicine News.