ClinicNet: machine learning for personalized clinical order set recommendations.

ABSTRACT:

Objective

This study assesses whether neural networks trained on electronic health record (EHR) data can anticipate what individual clinical orders and existing institutional order set templates clinicians will use more accurately than existing decision support tools.

Materials and methods

We process 57 624 patients worth of clinical event EHR data from 2008 to 2014. We train a feed-forward neural network (ClinicNet) and logistic regression applied to the traditional problem structure of predicting individual clinical items as well as our proposed workflow of predicting existing institutional order set template usage.

Results

ClinicNet predicts individual clinical orders (precision = 0.32, recall = 0.47) better than existing institutional order sets (precision = 0.15,

SUBMITTER: Wang JX 

PROVIDER: S-EPMC7382624 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature