Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.

ABSTRACT:

Background

The use of structured electronic health records in health care systems has grown rapidly. These systems collect huge amounts of patient information, including diagnosis codes representing temporal medical history. Sequential diagnostic information has proven valuable for predicting patient outcomes. However, the extent to which these types of data have been incorporated into deep learning (DL) models has not been examined.

Objective

This systematic review aims to describe the use of sequential diagnostic data in DL models, specifically to understand how these data are integrated, whether sample size improves performance, and whether the identified models are generalizable.

Methods

Relevant studies published up to May 15, 2023, were identified using 4 databa

SUBMITTER: Hama T 

PROVIDER: S-EPMC11962322 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature