The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer.

ABSTRACT:

Purpose

This study aimed to develop a machine learning model to retrospectively study and predict the recurrence risk of breast cancer patients after surgery by extracting the clinicopathological features of tumors from unstructured clinical electronic health record (EHR) data.

Methods

This retrospective cohort included 1,841 breast cancer patients who underwent surgical treatment. To extract the principal features associated with recurrence risk, the clinical notes and histopathology reports of patients were collected and feature engineering was used. Predictive models were next conducted based on this important information. All algorithms were implemented using Python software. The accuracy of prediction models was further verified in the test cohort. The area under the cu

SUBMITTER: Zeng L 

PROVIDER: S-EPMC10029918 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature