Using natural language processing and machine learning to identify breast cancer local recurrence.

ABSTRACT:

Background

Identifying local recurrences in breast cancer from patient data sets is important for clinical research and practice. Developing a model using natural language processing and machine learning to identify local recurrences in breast cancer patients can reduce the time-consuming work of a manual chart review.

Methods

We design a novel concept-based filter and a prediction model to detect local recurrences using EHRs. In the training dataset, we manually review a development corpus of 50 progress notes and extract partial sentences that indicate breast cancer local recurrence. We process these partial sentences to obtain a set of Unified Medical Language System (UMLS) concepts using MetaMap, and we call it positive concept set. We apply MetaMap on patients' progress

SUBMITTER: Zeng Z 

PROVIDER: S-EPMC6309052 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature