A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues

Shahab Shamshirband, Mahdis Fathi, Abdollah Dehzangi, Anthony Theodore Chronopoulos, Hamid Alinejad-Rokny

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

Abstract

In the last few years, the application of Machine Learning approaches like Deep Neural Network (DNN) models have become more attractive in the healthcare system given the rising complexity of the healthcare data. Machine Learning (ML) algorithms provide efficient and effective data analysis models to uncover hidden patterns and other meaningful information from the considerable amount of health data that conventional analytics are not able to discover in a reasonable time. In particular, Deep Learning (DL) techniques have been shown as promising methods in pattern recognition in the healthcare systems. Motivated by this consideration, the contribution of this paper is to investigate the deep learning approaches applied to healthcare systems by reviewing the cutting-edge network architectures, applications, and industrial trends. The goal is first to provide extensive insight into the application of deep learning models in healthcare solutions to bridge deep learning techniques and human healthcare interpretability. And then, to present the existing open challenges and future directions.

Original languageEnglish (US)
Article number103627
JournalJournal of Biomedical Informatics
Volume113
DOIs
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Computer Science Applications

Keywords

  • Deep neural network
  • Diagnostics tools
  • Health data analytics
  • Healthcare applications
  • Machine learning

Fingerprint Dive into the research topics of 'A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues'. Together they form a unique fingerprint.

Cite this