Identifying Breast Cancer Distant Recurrences from Electronic Health Records Using Machine Learning.

ABSTRACT: Accurately identifying distant recurrences in breast cancer from the Electronic Health Records (EHR) is important for both clinical care and secondary analysis. Although multiple applications have been developed for computational phenotyping in breast cancer, distant recurrence identification still relies heavily on manual chart review. In this study, we aim to develop a model that identifies distant recurrences in breast cancer using clinical narratives and structured data from EHR. We applied MetaMap to extract features from clinical narratives and also retrieved structured clinical data from EHR. Using these features, we trained a support vector machine model to identify distant recurrences in breast cancer patients. We trained the model using 1,396 double-annotated subjects and validat

SUBMITTER: Zeng Z 

PROVIDER: S-EPMC7678240 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature