Introduction

In the context of the intelligent era, the continuous development of the new generation of information technology gradually makes intelligent health services into sight of researchers. Intelligent healthcare has also become a frontier development direction in industries. The research in the field of intelligent healthcare has very important theoretical significance, application value, and economic benefits. Intelligent healthcare business has changed the traditional passive nursing, and promoted the active perception and preventive nursing of healthcare. Deep learning has played a huge role in the field of intelligent healthcare, and has gradually become a hot topic for researchers. The most widely used deep learning algorithms include Restricted Boltzmann Machines (RBM), Deep Belief Network (DBN), Convolutional Neural Networks (CNN), Recurrent Neural Network (RNN) and Stacked Denoising Auto Encoder (SDAE). These deep learning algorithms have promoted the further development of intelligent healthcare to some extent.

At present, research on intelligent healthcare is in rapid progress. Although some research results have been achieved, there are still some problems that need to be solved urgently. Firstly, in the current era of data explosion, numerous medical data emerge simultaneously. Meanwhile, there is also a considerable amount of false or garbage information, which affects the stable operation of the intelligent healthcare system and brings inconvenience to the medical diagnosis of patients. Secondly, privacy is also a problem that needs to be paid attention to in intelligent healthcare research. Personal medical diagnosis information often has certain privacy, so it is necessary to focus on the protection of privacy information in intelligent online consultation, so as to improve the application effect of intelligent medical diagnosis. Thirdly, the application of deep learning in intelligent healthcare at the present stage is generally concentrated in data mining, medical imaging diagnosis and so on. There are few efforts in the field of overall healthcare and the whole process of adjuvant therapy, medical diagnosis and postoperative nursing. In summary, it is essential and urgent to further expand the application scope of deep learning in intelligent healthcare, optimize the application effect, and establish a full-range intelligent health care system.

This Special Issue focuses on data mining, big data analysis technology, deep convolution networks, generate against networks (GAN), gradient boosted machines (GBM) and deep reinforcement learning (DRL) to be applied in healthcare practices, such as disease transmission prediction, auxiliary diagnosis, post-recovery assessment of disease, new drug research and development, identification and early warning of psychological illness, health management, medical image recognition.

Recommended topics (but not limited to)

The primary objective of this special issue is to provide a platform for researchers and scientists to share and discuss the most recent innovations, trends, and concerns, as well as practical challenges and solutions adopted in the field of scalable computing algorithms for material sciences.

• Exploring the GAN-based Intelligent Healthcare
• Analysis of the Application of GBN in Intelligent Healthcare
• Application of DRL in the Concrete Intelligent Healthcare
• Construction and Optimization of the Scheme of Full-range Intelligent Healthcare
• Application of Deep Learning in Medical Diagnosis, Treatment and Nursing
• Scheme Construction for Medical Big Data Analysis
• Key Information Extraction of Medical Big Data
• Privacy Data Protection of Online Healthcare
• Encrypted Transmission of Medical data Information
• Research on the Application of Deep Learning in Healthcare Management
• Research on Deep Learning for Medical Imaging Diagnosis
• Construction of a Global Intelligent Healthcare Network

Important dates

Submission deadline: 31 October, 2024

Authors notification:  31 January, 2025

Revision submission: 31 March, 2025

Submission guidelines

Original and unpublished works on any of the topics aforementioned or related are welcome. The SCPE journal has a rigorous peer-reviewing process and papers will be reviewed by at least two referees. All submitted papers must be formatted according to the journal's instructions, which can be found here.

During submission please select a Special Issue that you want to submit to and provide this information in the Comments for the Editor field.

Guest Editors

Lead: Shaofei Wu, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, China, email: 04005047@wit.edu.cn

Shaofei Wu was born in 1979. He was educated at Huazhong University of Science and Technology (Wuhan, China). During 2001-2004, he was a master of science in Computer Science and Technology at Huazhong University of Science and Technology (Wuhan, China) and He completed his PhD in Information Management at Huazhong University of Science and Technology (Wuhan, China) in 2009. He has been an associate professor researcher and supervisor of postgraduate at the School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, Hubei, China. His research focus on intelligent computing and evolutionary computing, image processing and pattern recognition, healthcare Informatics.

Hailong Li, Division of Neonatology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, email: hailongli_cchmc@hotmail.com

Dr. Li received Ph.D. degree in Computer Science from University of Cincinnati, Cincinnati, United States. Dr. Li is a Research Associate in the Division of Neonatology, Cincinnati Children's Hospital Medical Center. His current research interests focus on machine learning, especially deep learning, and pediatric medical image analysis.

Sen Fang, Division of Theoretical Computer Science, KTH Royal Institute of Technology, Sweden, email: senf@kth.se

Dr. Fang is a research engineer at Division of Theoretical Computer Science, KTH Royal Institute of Technology, Sweden. His research interests lie in the AI4SE, LLMs on Code, and empirical software engineering.