Combining structured and unstructured data for predictive models: a deep learning approach.

ABSTRACT:

Background

The broad adoption of electronic health records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. However, while many research studies utilize temporal structured data on predictive modeling, they typically neglect potentially valuable information in unstructured clinical notes. Integrating heterogeneous data types across EHRs through deep learning techniques may help improve the performance of prediction models.

Methods

In this research, we proposed 2 general-purpose multi-modal neural network architectures to enhance patient representation learning by comb

SUBMITTER: Zhang D 

PROVIDER: S-EPMC7596962 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature