Using natural language processing to construct a metastatic breast cancer cohort from linked cancer registry and electronic medical records data.

ABSTRACT:

Objectives

Most population-based cancer databases lack information on metastatic recurrence. Electronic medical records (EMR) and cancer registries contain complementary information on cancer diagnosis, treatment and outcome, yet are rarely used synergistically. To construct a cohort of metastatic breast cancer (MBC) patients, we applied natural language processing techniques within a semisupervised machine learning framework to linked EMR-California Cancer Registry (CCR) data.

Materials and methods

We studied all female patients treated at Stanford Health Care with an incident breast cancer diagnosis from 2000 to 2014. Our database consisted of structured fields and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology

SUBMITTER: Ling AY 

PROVIDER: S-EPMC6994019 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature