Cohort selection for clinical trials using deep learning models.

ABSTRACT:

Objective

The goal of the 2018 n2c2 shared task on cohort selection for clinical trials (track 1) is to identify which patients meet the selection criteria for clinical trials. Cohort selection is a particularly demanding task to which natural language processing and deep learning can make a valuable contribution. Our goal is to evaluate several deep learning architectures to deal with this task.

Materials and methods

Cohort selection can be formulated as a multilabeling problem whose goal is to determine which criteria are met for each patient record. We explore several deep learning architectures such as a simple convolutional neural network (CNN), a deep CNN, a recurrent neural network (RNN), and CNN-RNN hybrid architecture. Although our architectures are similar to those

SUBMITTER: Segura-Bedmar I 

PROVIDER: S-EPMC6798560 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature