Journal of Healthcare Engineering
 Journal metrics
Acceptance rate37%
Submission to final decision99 days
Acceptance to publication48 days
CiteScore4.600
Impact Factor2.682

Article of the Year 2020

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

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 Journal profile

Journal of Healthcare Engineering provides a vehicle for the exchange of advanced knowledge, emerging technologies, and innovative ideas related to all aspects of engineering involved in healthcare delivery processes and systems.

 Editor spotlight

Chief Editor, Professor Zollo, has research expertise in neuro-robotics and biomedical technologies for neuroscience, rehabilitation and assistance robotics, and robotic and mechatronic devices for personal assistance and service robotics.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Research Article

Visual Information Features and Machine Learning for Wushu Arts Tracking

Martial arts tracking is an important research topic in computer vision and artificial intelligence. It has extensive and vital applications in video monitoring, interactive animation and 3D simulation, motion capture, and advanced human-computer interaction. However, due to the change of martial arts’ body posture, clothing variability, and light mixing, the appearance changes significantly. As a result, accurate posture tracking becomes a complicated problem. A solution to this complicated problem is studied in this paper. The proposed solution improves the accuracy of martial arts tracking by the image representation method of martial arts tracking. This method is based on the second-generation strip wave transform and applies it to the video martial arts tracking based on the machine learning method.

Research Article

Study on Model Iterative Reconstruction Algorithm vs. Filter Back Projection Algorithm for Diagnosis of Acute Cerebral Infarction Using CT Images

The aim was to explore the application value of computed tomography (CT) perfusion (CTP) imaging based on the iterative model reconstruction (IMR) in the diagnosis of acute cerebral infarction (ACI). 80 patients with ACI, admitted to hospital, were selected as the research objects and divided randomly into a routine treatment group (group A) and a low-dose group (group B) (each group with 40 patients). Patients in group A were scanned at 80 kV–150 mAs, and the traditional filtered back projection (FBP) algorithm was employed to reconstruct the images; besides, 80 kV–30 mAs was adopted to scan the patients in group B, and the images were reconstructed by IMR1, IMR2, IMR3, iDose4 (a kind of hybrid iterative reconstruction technology), and FBP, respectively. The application values of different algorithms were evaluated by CTP based on the collected CTP images of patients and detecting indicators. The results showed that the gray and white matter CT value, SD value, SNR, CNR, and subjective image scores of patients in group B were basically consistent with those of group A ( > 0.05) after the IMR1 reconstruction, and the CT and SD of gray and white matter in patients from group B reduced steeply ( < 0.05), while SNR and CNR increased dramatically after IMR2 and IMR3 reconstruction in contrast to group A ( < 0.05). Furthermore, the cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) of contrast agent, and time to peak (TTP) of contrast agent in patients from group B after iDose4 and IMR reconstruction were basically the same as those of group A ( > 0.05). Therefore, IMR combined with low-dose CTP could obtain high-quality CTP images of the brain with stable perfusion indicators and low radiation dose, which could be clinically applied in the diagnosis of ACI.

Research Article

Application of Intelligent Nursing Information System in Emergency Nursing Management

This paper is combined with the intelligent nursing information system to build the emergency nursing platform architecture, from the system emergency procedures, system functionality, network environment deployment, and database design aspects of the discussion. Based on hospital information security, the nursing monitoring system of the intelligent nursing information system is constructed to realize network communication, which is clear and intuitive. The intelligent information system is applied to safety control, medical order information, condition information, and information inquiry, which can save working time and complete the rapid transmission and accurate execution of medical order, making the network communication of medical care more quick and convenient and maximizing the overall efficiency. Based on the disordered phenomenon of registration triage, the Relief algorithm is used to classify the aetiology and triage, and the combination of medical advice, information query, and IT technology is optimized, so as to eliminate the phenomenon of round diagnosis, insert number, and improve the medical environment of waiting for diagnosis, taking medicine, examination, and testing. Finally, through the testing of system information security, information traceability, and rapid information query, the problems in nursing management have been basically solved.

Research Article

A Core Drug Discovery Framework from Large-Scale Literature for Cold Pathogenic Disease Treatment in Traditional Chinese Medicine

Cold pathogenic disease is a widespread disease in traditional Chinese medicine, which includes influenza and respiratory infection associated with high incidence and mortality. Discovering effective core drugs in Chinese medicine prescriptions for treating the disease and reducing patients’ symptoms has attracted great interest. In this paper, we explore the core drugs for curing various syndromes of cold pathogenic disease from large-scale literature. We propose a core drug discovery framework incorporating word embedding and community detection algorithms, which contains three parts: disease corpus construction, drug network generation, and core drug discovery. First, disease corpus is established by collecting and preprocessing large-scale literature about the Chinese medicine treatment of cold pathogenic disease from China National Knowledge Infrastructure. Second, we adopt the Chinese word embedding model SSP2VEC for mining the drug implication implied in the literature; then, a drug network is established by the semantic similarity among drugs. Third, the community detection method COPRA based on label propagation is adopted to reveal drug communities and identify core drugs in the drug network. We compute the community size, closeness centrality, and degree distributions of the drug network to analyse the patterns of core drugs. We acquire 4681 literature from China national knowledge infrastructure. Twelve significant drug communities are discovered, in which the top-10 drugs in every drug community are recognized as core drugs with high accuracy, and four classical prescriptions for treating different syndromes of cold pathogenic disease are discovered. The proposed framework can identify effective core drugs for curing cold pathogenic disease, and the research can help doctors to verify the compatibility laws of Chinese medicine prescriptions.

Research Article

Drug Packaging Management Based on the Effect of Medical Images on the Intracellular Polysaccharide Synthesis and Antivertigo Activity of Phalaenopsis

Many clinically important drugs come directly or indirectly from higher plants. People are increasingly aware of the role of the human immune system in maintaining good health. Diseases related to physical dysfunction, such as vertigo, have attracted increasing attention from medical researchers and clinicians. In this paper, some compounds isolated and identified from medicinal fomes showed promising antivertigo properties. Medical images were used to classify and synthesize polysaccharides in the management of drug subpackages of Cladosporium intracellular polysaccharides. The scientific explanation of how these compounds work in animal and human systems is increasing exponentially. Studies have found that all of these compounds can enhance the innate and adaptive immune responses of the host and activate various immune cells that are important for maintaining homeostasis, such as host cells and chemical messengers, triggering complement and acute phase reactions. The antivertigo compounds derived from the intracellular polysaccharides of Phellinus mucronatus had an activity interference of 35% without drug subpackage. Although the antivertigo activity of many intracellular polysaccharides from Fovea xylostella can reach 86%, only a few of them have been proved to have antivertigo activity. In addition, they can be considered as multicytokine inducers that can induce the expression of various immune-regulatory cytokines and cytokine receptor genes. Lymphocytes that control antibody production and cell-mediated cytotoxicity are also stimulated.

Research Article

Role of Folic Acid Drugs in the Treatment with Antithrombotic and Anticoagulant Drugs for Patients with Cardiovascular Diseases Based on the Analysis of Virtual Reality Medical Data

In recent years, with the continuous progress and development of science and technology and the increasing maturity of medical technology, the incidence of cardiovascular diseases has gradually increased with the age of the population. In the case of cardiovascular disease, proper anticoagulant therapy can effectively prevent bleeding in the occurrence of events, so a more effective treatment of cardiovascular disease is considered a difficult problem to overcome. Therefore, this article proposes the role of folic acid drugs based on virtual reality medical data analysis in the treatment of cardiovascular disease patients with antithrombotic and anticoagulant drugs, in order to improve providing help for cardiovascular disease. This study selected patients with cardiovascular disease who were admitted to the hospital and extracted 100 patients with complete data and a one-year follow-up period, covering the overall status of the patients’ cardiovascular risk factors, cardiovascular disease degree, and the occurrence of major cardiovascular adverse events. During the follow-up period, we analyzed the specific status of major cardiovascular adverse events and the occurrence of bleeding events and compared and analyzed the effects of folic acid drugs on the treatment with antithrombotic and anticoagulant drugs in patients with cardiovascular disease. Experiments have proved that the differences in the degree of cardiovascular stenosis and the number of cardiovascular disease vessels in the four groups are statistically significant (). The degree of cardiovascular stenosis in group D was lighter than that in groups A, B, and C, and the number of cardiovascular lesions was also less than that in groups A, B, and C. The differences were statistically significant (). This indicates that folic acid can effectively treat cardiovascular stenosis, prevent cardiovascular disease, and then treat patients with cardiovascular disease with antithrombotic and anticoagulant drugs. It provides an important basis for accurate clinical diagnosis and treatment.

Journal of Healthcare Engineering
 Journal metrics
Acceptance rate37%
Submission to final decision99 days
Acceptance to publication48 days
CiteScore4.600
Impact Factor2.682
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.