A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction

A fundamental obstacle to healthcare transformation continues to be the acquisition of knowledge and insightful data from complex, high dimensional, and heterogeneous biological data. As technology has improved, a wide variety of data sources, including omics data, imaging data, and health records, have been available for use in healthcare research contexts. Electronic health records (EHRs), which are digitalized versions of medical records, have given researchers a significant chance to create computational methods for analyzing healthcare data. EHR systems typically keep track of all the data relating to a patient’s medical history, including clinical notes, demographic background, and diagnosis details. EHR data can offer valuable insights and support doctors in making better decisions related to disease and diagnostic forecasts. As a result, several academics use deep learning to forecast diseases and track health trajectories in EHR. Recent advances in deep learning technology have produced innovative and practical paradigms for building end-to-end learning models. However, scholars have limited access to online HER databases, and there is an inherent need to address this issue. This research examines deep learning models, their architectures, and readily accessible EHR online databases. The goal of this paper is to examine how various architectures, models, and databases differ in terms of features and usability. It is anticipated that the outcomes of this review will lead to the development of more robust deep learning models that facilitate medical decision-making processes based on EHR data and inform efforts to support the selection of architectures, models, and databases for specific research purposes.

• Publication year

  2024

• Venue

  Artificial Intelligence Review

• Publication date

  2024-08-13

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1007/s10462-024-10876-2
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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