Shareable artificial intelligence to extract cancer outcomes from electronic health records for precision oncology research.

ABSTRACT: Databases that link molecular data to clinical outcomes can inform precision cancer research into novel prognostic and predictive biomarkers. However, outside of clinical trials, cancer outcomes are typically recorded only in text form within electronic health records (EHRs). Artificial intelligence (AI) models have been trained to extract outcomes from individual EHRs. However, patient privacy restrictions have historically precluded dissemination of these models beyond the centers at which they were trained. In this study, the vulnerability of text classification models trained directly on protected health information to membership inference attacks is confirmed. A teacher-student distillation approach is applied to develop shareable models for annotating outcomes from imaging reports an

SUBMITTER: Kehl KL 

PROVIDER: S-EPMC11557593 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature