Assessment of Deep Natural Language Processing in Ascertaining Oncologic Outcomes From Radiology Reports.

ABSTRACT:

Importance

A rapid learning health care system for oncology will require scalable methods for extracting clinical end points from electronic health records (EHRs). Outside of clinical trials, end points such as cancer progression and response are not routinely encoded into structured data.

Objective

To determine whether deep natural language processing can extract relevant cancer outcomes from radiologic reports, a ubiquitous but unstructured EHR data source.

Design, setting, and participants

A retrospective cohort study evaluated 1112 patients who underwent tumor genotyping for a diagnosis of lung cancer and participated in the Dana-Farber Cancer Institute PROFILE study from June 26, 2013, to July 2, 2018.

Exposures

Patients were divided into curation and rese

SUBMITTER: Kehl KL 

PROVIDER: S-EPMC6659158 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature