Ensuring fair, safe, and interpretable artificial intelligence-based prediction tools in a real-world oncological setting.

ABSTRACT:

Background

Cancer patients often experience treatment-related symptoms which, if uncontrolled, may require emergency department admission. We developed models identifying breast or genitourinary cancer patients at the risk of attending emergency department (ED) within 30-days and demonstrated the development, validation, and proactive approach to in-production monitoring of an artificial intelligence-based predictive model during a 3-month simulated deployment at a cancer hospital in the United States.

Methods

We used routinely-collected electronic health record data to develop our predictive models. We evaluated models including a variational autoencoder k-nearest neighbors algorithm (VAE-kNN) and model behaviors with a sample containing 84,138 observations from 28,369 pati

SUBMITTER: George R 

PROVIDER: S-EPMC10287624 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature