Analysing the Structure of Electronic Health Record (EHR) Using Deep Learning

Authors

  • Rugved V Deolekar, Sunil B Wankhade

Keywords:

Adverse drug events, Clinicians, Drug malfunctioning, EHR, EPR, Healthcare, Medico-Legal, Medical Non-adherence, Medical records

Abstract

This paper proposes a novel approach for analysing the structure of Electronic Health Records (EHRs) using deep learning. Different types of EHRs and their respective structures are discussed. The authors then explore different approaches to applying deep learning to the task of analysing EHRs. This includes an overview of deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The authors propose a framework for applying deep learning to the task of EHR structure analysis. The framework consists of a pre-processing step to clean the data and a model to classify the EHRs. Finally, the authors provide a case study to demonstrate the efficacy of their proposed approach. Deep learning is a powerful and versatile tool for analysing the structure of an Electronic Health Record (EHR). Deep learning can be used to identify patterns and relationships between EHR data elements such as patient demographics, diagnoses, medications, and laboratory results. This information can then be used to generate predictive models and identify potential areas of improvement within the healthcare system. Deep learning techniques can also be leveraged to gain insights into the interactions between medical entities and healthcare processes, such as disease comorbidities or medication-related adverse events. By understanding the structure of an EHR, deep learning can provide valuable insights into the quality of care and help clinicians make more informed decisions.

 

Published

2023-03-22

How to Cite

Rugved V Deolekar, Sunil B Wankhade. (2023). Analysing the Structure of Electronic Health Record (EHR) Using Deep Learning. SJIS-P, 35(1), 490–494. Retrieved from http://sjis.scandinavian-iris.org/index.php/sjis/article/view/329

Issue

Section

Articles