Subjects -> HEALTH AND SAFETY (Total: 1566 journals)
    - CIVIL DEFENSE (22 journals)
    - DRUG ABUSE AND ALCOHOLISM (86 journals)
    - HEALTH AND SAFETY (744 journals)
    - HEALTH FACILITIES AND ADMINISTRATION (390 journals)
    - OCCUPATIONAL HEALTH AND SAFETY (109 journals)
    - PHYSICAL FITNESS AND HYGIENE (133 journals)
    - WOMEN'S HEALTH (82 journals)

HEALTH FACILITIES AND ADMINISTRATION (390 journals)                  1 2 | Last

Showing 1 - 200 of 401 Journals sorted alphabetically
Academy of Health Care Management Journal     Full-text available via subscription   (Followers: 13)
ACI Open     Open Access  
Acta Bioquimica Clinica Latinoamericana     Open Access   (Followers: 1)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 22)
Adnan Menderes Üniversitesi Sağlık Bilimleri Fakültesi Dergisi     Open Access   (Followers: 1)
Advanced Healthcare Materials     Hybrid Journal   (Followers: 17)
Advances in Dual Diagnosis     Hybrid Journal   (Followers: 48)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 26)
Advances in Medical Education and Practice     Open Access   (Followers: 34)
Advances in Mental Health and Intellectual Disabilities     Hybrid Journal   (Followers: 89)
Advances in Nursing Science     Hybrid Journal   (Followers: 43)
Advances in Simulation     Open Access   (Followers: 7)
African Journal of Primary Health Care & Family Medicine     Open Access   (Followers: 6)
AIDS and Behavior     Hybrid Journal   (Followers: 18)
American Journal of Hospice and Palliative Medicine     Hybrid Journal   (Followers: 48)
American Journal of Managed Care     Full-text available via subscription   (Followers: 13)
Analytical Methods     Full-text available via subscription   (Followers: 14)
Anthropologie et santé     Open Access   (Followers: 5)
Applied Clinical Informatics     Hybrid Journal   (Followers: 5)
Applied Health Economics and Health Policy     Full-text available via subscription   (Followers: 24)
Applied Research in Quality of Life     Hybrid Journal   (Followers: 13)
Archives of Public Health     Open Access   (Followers: 13)
Asian Journal of Health     Open Access   (Followers: 4)
Australasian Journal of Paramedicine     Open Access   (Followers: 7)
Australian and New Zealand Journal of Public Health     Hybrid Journal   (Followers: 17)
Australian Health Review     Hybrid Journal   (Followers: 7)
Australian Journal of Primary Health     Hybrid Journal  
Australian Journal of Rural Health     Hybrid Journal   (Followers: 18)
Autism     Hybrid Journal   (Followers: 349)
Avicenna     Open Access   (Followers: 3)
Balint Journal     Hybrid Journal   (Followers: 2)
Bereavement Care     Hybrid Journal   (Followers: 13)
BJR     Hybrid Journal   (Followers: 21)
BMC Medical Informatics and Decision Making     Open Access   (Followers: 25)
BMC Oral Health     Open Access   (Followers: 7)
BMJ Leader     Hybrid Journal  
BMJ Quality & Safety     Hybrid Journal   (Followers: 69)
BMJ Supportive & Palliative Care     Hybrid Journal   (Followers: 50)
British Journal of Healthcare Assistants     Full-text available via subscription   (Followers: 33)
British Journal of Healthcare Management     Full-text available via subscription   (Followers: 19)
British Journal of Hospital Medicine     Full-text available via subscription   (Followers: 18)
British Journal of Nursing     Full-text available via subscription   (Followers: 294)
British Journal of School Nursing     Full-text available via subscription   (Followers: 14)
Bruce R Hopkins' Nonprofit Counsel     Hybrid Journal   (Followers: 2)
Building Better Healthcare     Full-text available via subscription   (Followers: 1)
Canadian Nurse     Full-text available via subscription   (Followers: 8)
Cardiac Electrophysiology Clinics     Full-text available via subscription   (Followers: 1)
Children and Schools     Hybrid Journal   (Followers: 8)
Chinese Medical Record English Edition     Hybrid Journal  
CIN : Computers Informatics Nursing     Hybrid Journal   (Followers: 11)
Clinical Audit     Open Access   (Followers: 4)
Clinics and Practice     Open Access  
Cognition, Technology & Work     Hybrid Journal   (Followers: 14)
Communication & Medicine     Hybrid Journal   (Followers: 5)
Community Based Medical Journal     Open Access  
Conflict and Health     Open Access   (Followers: 8)
Contemporary Nurse : A Journal for the Australian Nursing Profession     Hybrid Journal   (Followers: 7)
Critical Public Health     Hybrid Journal   (Followers: 26)
Culture, Health & Sexuality: An International Journal for Research, Intervention and Care     Hybrid Journal   (Followers: 17)
Current Opinion in Supportive and Palliative Care     Hybrid Journal   (Followers: 28)
Das Gesundheitswesen     Hybrid Journal   (Followers: 10)
Death Studies     Hybrid Journal   (Followers: 22)
Dental Nursing     Full-text available via subscription   (Followers: 3)
Disaster Health     Hybrid Journal   (Followers: 1)
DoctorConsult - The Journal. Wissen für Klinik und Praxis     Full-text available via subscription  
Droit, Déontologie & Soin     Full-text available via subscription   (Followers: 3)
E-Health Telecommunication Systems and Networks     Open Access   (Followers: 2)
East and Central African Journal of Surgery     Open Access  
Éducation thérapeutique du patient     Full-text available via subscription   (Followers: 1)
eGEMs     Open Access  
Emergency Radiology     Hybrid Journal   (Followers: 10)
Enfermería Clínica     Full-text available via subscription   (Followers: 3)
Epidemiologic Methods     Hybrid Journal   (Followers: 4)
Ergonomics     Hybrid Journal   (Followers: 24)
Escola Anna Nery     Open Access   (Followers: 1)
Ethnicity & Health     Hybrid Journal   (Followers: 14)
European Journal of Public Health     Hybrid Journal   (Followers: 27)
European Journal of Work and Organizational Psychology     Hybrid Journal   (Followers: 35)
European Research in Telemedicine / La Recherche Européenne en Télémédecine     Full-text available via subscription   (Followers: 2)
Evaluation & the Health Professions     Hybrid Journal   (Followers: 10)
Evidence-Based Nursing     Hybrid Journal   (Followers: 74)
Evolution, Medicine, and Public Health     Open Access   (Followers: 12)
Expert Opinion on Therapeutic Patents     Hybrid Journal   (Followers: 12)
Families, Systems, & Health     Full-text available via subscription   (Followers: 9)
Family Practice Management     Full-text available via subscription   (Followers: 5)
Focus on Health Professional Education : A Multi-disciplinary Journal     Full-text available via subscription   (Followers: 7)
Frontiers in Public Health Services and Systems Research     Open Access   (Followers: 5)
Future Hospital Journal     Full-text available via subscription   (Followers: 2)
Gastrointestinal Nursing     Full-text available via subscription   (Followers: 5)
Geron     Full-text available via subscription  
Global & Regional Health Technology Assessment     Open Access   (Followers: 1)
Global Health Action     Open Access   (Followers: 12)
Global Health Management Journal (GHMJ)     Open Access   (Followers: 1)
Global Health Research and Policy     Open Access   (Followers: 4)
Global Journal of Hospital Administration     Open Access   (Followers: 1)
Global Public Health: An International Journal for Research, Policy and Practice     Hybrid Journal   (Followers: 21)
Globalization and Health     Open Access   (Followers: 9)
Handbook of Practice Management     Hybrid Journal   (Followers: 2)
Health     Open Access   (Followers: 5)
Health & Social Care In the Community     Hybrid Journal   (Followers: 54)
Health : An Interdisciplinary Journal for the Social Study of Health, Illness and Medicine     Hybrid Journal   (Followers: 16)
Health and Interprofessional Practice     Open Access   (Followers: 6)
Health and Technology     Hybrid Journal   (Followers: 4)
Health Care Analysis     Hybrid Journal   (Followers: 17)
Health Care Management Review     Hybrid Journal   (Followers: 16)
Health Economics     Hybrid Journal   (Followers: 59)
Health Expectations     Open Access   (Followers: 16)
Health Facilities Management     Free   (Followers: 10)
Health Informatics Journal     Hybrid Journal   (Followers: 28)
Health Information : Jurnal Penelitian     Open Access   (Followers: 6)
Health Information Science and Systems     Open Access   (Followers: 4)
Health Policy and Management     Open Access   (Followers: 7)
Health Policy and Planning     Hybrid Journal   (Followers: 27)
Health Professions Education     Open Access   (Followers: 3)
Health Promotion International     Hybrid Journal   (Followers: 28)
Health Promotion Practice     Hybrid Journal   (Followers: 18)
Health Psychology     Full-text available via subscription   (Followers: 62)
Health Psychology Review     Hybrid Journal   (Followers: 46)
Health Reform Observer : Observatoire des Réformes de Santé     Open Access   (Followers: 2)
Health Research Policy and Systems     Open Access   (Followers: 16)
Health Science Journal of Indonesia     Open Access   (Followers: 2)
Health Services Research and Managerial Epidemiology     Open Access   (Followers: 3)
Health, Risk & Society     Hybrid Journal   (Followers: 14)
Healthcare : The Journal of Delivery Science and Innovation     Full-text available via subscription   (Followers: 1)
Healthcare Financial Management     Full-text available via subscription   (Followers: 4)
Healthcare in Low-resource Settings     Open Access   (Followers: 1)
Healthcare Management Forum     Hybrid Journal   (Followers: 8)
Healthcare Policy / Politiques de Santé     Full-text available via subscription   (Followers: 5)
Healthcare Quarterly     Full-text available via subscription   (Followers: 10)
Healthcare Risk Management     Full-text available via subscription   (Followers: 5)
HealthcarePapers     Full-text available via subscription   (Followers: 2)
Hispanic Health Care International     Full-text available via subscription  
História, Ciências, Saúde - Manguinhos     Open Access   (Followers: 2)
Hong Kong Journal of Social Work, The     Hybrid Journal   (Followers: 3)
Hospital     Open Access   (Followers: 3)
Hospital a Domicilio     Open Access  
Hospital Infection Control & Prevention     Full-text available via subscription   (Followers: 15)
Hospital Medicine Clinics     Full-text available via subscription   (Followers: 2)
Hospital Peer Review     Full-text available via subscription   (Followers: 1)
Hospital Pharmacy     Partially Free   (Followers: 18)
Hospital Practice     Hybrid Journal   (Followers: 2)
Hospital Practices and Research     Open Access  
Housing, Care and Support     Hybrid Journal   (Followers: 9)
Human Factors : The Journal of the Human Factors and Ergonomics Society     Full-text available via subscription   (Followers: 39)
Human Resources for Health     Open Access   (Followers: 12)
ICU Director     Hybrid Journal  
Ids Practice Papers     Hybrid Journal  
IEEE Pulse     Hybrid Journal   (Followers: 5)
IISE Transactions on Healthcare Systems Engineering     Hybrid Journal   (Followers: 2)
Independent Nurse     Full-text available via subscription   (Followers: 3)
Index de Enfermeria     Open Access   (Followers: 7)
Indian Journal of Public Health     Open Access   (Followers: 1)
Informatics for Health and Social Care     Hybrid Journal   (Followers: 10)
Innovation and Entrepreneurship in Health     Open Access   (Followers: 1)
INQUIRY : The Journal of Health Care Organization, Provision, and Financing     Open Access   (Followers: 1)
Interface - Comunicação, Saúde, Educação     Open Access   (Followers: 1)
International Archives of Health Sciences     Open Access  
International Journal for Equity in Health     Open Access   (Followers: 9)
International Journal for Quality in Health Care     Hybrid Journal   (Followers: 41)
International Journal of Care Coordination     Hybrid Journal   (Followers: 7)
International Journal of Computers in Healthcare     Hybrid Journal   (Followers: 3)
International Journal of Electronic Healthcare     Hybrid Journal   (Followers: 2)
International Journal of Environmental Research and Public Health     Open Access   (Followers: 27)
International Journal of Health Administration and Education Congress (Sanitas Magisterium)     Open Access  
International Journal of Health Care Quality Assurance     Hybrid Journal   (Followers: 15)
International Journal of Health Economics and Management     Hybrid Journal   (Followers: 13)
International Journal of Health Governance     Hybrid Journal   (Followers: 27)
International Journal of Health Planning and Management     Hybrid Journal   (Followers: 6)
International Journal of Health Sciences Education     Open Access   (Followers: 2)
International Journal of Health Services Research and Policy     Open Access   (Followers: 1)
International Journal of Health System and Disaster Management     Open Access   (Followers: 3)
International Journal of Healthcare     Open Access   (Followers: 1)
International Journal of Healthcare Technology and Management     Hybrid Journal   (Followers: 7)
International Journal of Hospital Research     Open Access  
International Journal of Human Factors and Ergonomics     Hybrid Journal   (Followers: 20)
International Journal of Human Rights in Healthcare     Hybrid Journal   (Followers: 5)
International Journal of Medicine and Public Health     Open Access   (Followers: 6)
International Journal of Migration, Health and Social Care     Hybrid Journal   (Followers: 12)
International Journal of Occupational and Environmental Medicine, The     Open Access   (Followers: 15)
International Journal of Palliative Nursing     Full-text available via subscription   (Followers: 32)
International Journal of Positive Behavioural Support     Full-text available via subscription   (Followers: 38)
International Journal of Prisoner Health     Hybrid Journal   (Followers: 14)
International Journal of Privacy and Health Information Management     Full-text available via subscription   (Followers: 3)
International Journal of Public and Private Healthcare Management and Economics     Full-text available via subscription   (Followers: 4)
International Journal of Qualitative Studies on Health and Well-Being     Open Access   (Followers: 22)
International Journal of Reliable and Quality E-Healthcare     Full-text available via subscription   (Followers: 1)
International Journal of Research in Nursing     Open Access   (Followers: 12)
International Journal of Technology Assessment in Health Care     Hybrid Journal   (Followers: 16)
International Journal of Telemedicine and Clinical Practices     Hybrid Journal   (Followers: 5)
International Journal of Telework and Telecommuting Technologies     Full-text available via subscription  
International Journal of Therapy and Rehabilitation     Full-text available via subscription   (Followers: 42)
International Journal of User-Driven Healthcare     Full-text available via subscription   (Followers: 1)
International Journal on Disability and Human Development     Hybrid Journal   (Followers: 23)
Irish Journal of Paramedicine     Open Access   (Followers: 3)
JAAPA     Hybrid Journal   (Followers: 3)
Jaffna Medical Journal     Open Access  
Joint Commission Journal on Quality and Patient Safety     Hybrid Journal   (Followers: 41)
Journal for Healthcare Quality     Hybrid Journal   (Followers: 28)
Journal of Advanced Nursing     Hybrid Journal   (Followers: 250)
Journal of Advances in Medical Education & Professionalism     Open Access   (Followers: 10)

        1 2 | Last

Similar Journals
Journal Cover
Health Information Science and Systems
Number of Followers: 4  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2047-2501
Published by Springer-Verlag Homepage  [2653 journals]
  • How can social media analytics assist authorities in pandemic-related
           policy decisions' Insights from Australian states and territories

    • Abstract: Background and objectives Due to COVID-19, various countries introduced lockdowns and limited citizen movements. These restrictions triggered an increased use of digital technologies and platforms by the public. This provides an opportunity for the authorities to capture public perceptions on COVID-19 from social media channels to make informed decisions. The use of social media analytics during pandemics for decision-making, however, is an understudied area of research. Thus, this study aims to generate insights into how social media analytics can assist authorities in pandemic-related policy decisions. Methods This study involved a social media analysis approach—i.e., systematic geo-Twitter analysis—that contains descriptive, content, sentiment, and spatial analyses. Australian states and territories are selected as the case study context for the empirical investigation. This study collected 96,666 geotagged tweets (originated from Australia between 1 January and 4 May 2020), and analysed 35,969 of them after data cleaning. Results The findings disclose that: (a) Social media analytics is an efficient approach to capture the attitudes and perceptions of the public during a pandemic; (b) Crowdsourced social media data can guide interventions and decisions of the authorities during a pandemic, and; (c) Effective use of government social media channels can help the public to follow the introduced measures/restrictions. Conclusion The findings are invaluable for authorities to understand community perceptions and identify communities in needs and demands in a pandemic situation, where authorities are not in a position to conduct direct and lengthily public consultations.
      PubDate: 2020-10-15
      DOI: 10.1007/s13755-020-00121-9
       
  • Identifying health correlates of intimate partner violence against
           pregnant women

    • Abstract: Purpose Violence against women during pregnancy is a serious public health concern due to its significant adverse health consequences for both the mother and the baby. This study aims to systematically identify common health problems and synergistic health correlates of intimate partner violence (IPV) that specifically affect pregnant women. Methods We mine large-scale electronic health record (EHR) data from the IBM Explorys database to identify health problems that are prevalent in both IPV and pregnancy populations, as well those that are synergistically associated with the presence of IPV during pregnancy. For this purpose, we develop methods that enhance the statistical reliability of identified patterns by constructing confidence intervals that take into account systematic bias and measurement errors in addition to the variance in estimation. Results We identify with high confidence 668 and 2750 terms that are respectively prevalent in respectively IPV and pregnancy populations. Of these terms, 279 are common. We also identify 16 synergistic health correlates with high confidence. Our results suggest that mental health, substance abuse, and genitourinary complications are prevalent among pregnant women exposed to IPV. The synergistic terms we identify reveal potential conditions that can be direct consequences of trauma (e.g., tibial fracture), long-term health consequences (e.g., chronic rhinitis), markers associated with the demograhics of affected populations (e.g., acne), and risk factors that potentially increase vulnerability during pregnancy (e.g., disorders of attention and motor control). Conclusions Our results indicate that IPV significantly affects the well-being of pregnant women in multiple ways. The findings of this study can be useful for screening of IPV in pregnant women. Finally, the methodology presented here can also be useful for investigating the synergy between other medical conditions using EHR databases with privacy constraints.
      PubDate: 2020-10-15
      DOI: 10.1007/s13755-020-00124-6
       
  • Impact of COVID-19 prevalence and mode of transmission on mortality cases
           over WHO regions

    • Abstract: With the current outbreak of coronavirus disease 2019 (COVID-19), countries have been on rising preparedness to detect and isolate any imported and locally transmitted cases of the disease. It is observed that mode of transmission of the disease varies from one country to the other. Recent studies have shown that COVID-19 cases are not influenced by race and weather conditions. In this study, effect of modes of transmission of COVID-19 is considered with respect to prevalence and mortality counts in World Health Organisation (WHO) regions. Also, a negative binomial model is formulated for new death cases in all WHO regions as a function of confirmed cases, confirmed new cases, total deaths and modes of transmission, with the goal of identifying a model that predicts the total new death cases the best. Results from this study show that there is strong linear relationship among the COVID-19 confirmed cases, total new deaths and mode of transmission in all WHO regions. Findings highlight the significant roles of modes of transmission on total new death cases over WHO regions. Mode of transmission based on community transmission and clusters of cases significantly affects the number of new deaths in WHO regions. Vuong test shows that the formulated negative binomial model fits the data better than the null model.
      PubDate: 2020-10-15
      DOI: 10.1007/s13755-020-00127-3
       
  • Lightme: analysing language in internet support groups for mental health

    • Abstract: Background Assisting moderators to triage harmful posts in Internet Support Groups is relevant to ensure its safe use. Automated text classification methods analysing the language expressed in posts of online forums is a promising solution. Methods Natural Language Processing and Machine Learning technologies were used to build a triage post classifier using a dataset from Reachout.com mental health forum for young people. Results When comparing with the state-of-the-art, a solution mainly based on features from lexical resources, received the best classification performance for the crisis posts (52%), which is the most severe class. Six salient linguistic characteristics were found when analysing the crisis post; (1) posts expressing hopelessness, (2) short posts expressing concise negative emotional responses, (3) long posts expressing variations of emotions, (4) posts expressing dissatisfaction with available health services, (5) posts utilising storytelling, and (6) posts expressing users seeking advice from peers during a crisis. Conclusion It is possible to build a competitive triage classifier using features derived only from the textual content of the post. Further research needs to be done in order to translate our quantitative and qualitative findings into features, as it may improve overall performance.
      PubDate: 2020-10-13
      DOI: 10.1007/s13755-020-00115-7
       
  • Automated epilepsy detection techniques from electroencephalogram signals:
           a review study

    • Abstract: Epilepsy is a serious neurological condition which contemplates as top 5 reasons for avoidable mortality from ages 5–29 in the worldwide. The avoidable deaths due to epilepsy can be reduced by developing efficient automated epilepsy detection or prediction machines or software. To develop an automated epilepsy detection framework, it is essential to properly understand the existing techniques and their benefit as well as detriment also. This paper aims to provide insight on the information about the existing epilepsy detection and classification techniques as they are crucial for supporting clinical-decision in the course of epilepsy treatment. This review study accentuate on the existing epilepsy detection approaches and their drawbacks. This information presented in this article will be helpful to the neuroscientist, researchers as well as to technicians for assisting them in selecting the reliable and appropriate techniques for analyzing epilepsy and developing an automated software system of epilepsy identification.
      PubDate: 2020-10-12
      DOI: 10.1007/s13755-020-00129-1
       
  • Improving accessibility of the Australian My Health Records while
           preserving privacy and security of the system

    • Abstract: Australian My Health Record (MyHR) is a significant development in empowering patients, allowing them to access their summarised health information themselves and to share the information with all health care providers involved in their care. Consequently, the MyHR system must enable efficient availability of meaningful, accurate, and complete data to assist an improved clinical administration of a patient. However, while enabling this, protecting data privacy and ensuring security in the MyHR system has become a major concern because of its consequences in promoting high standards of patient care. In this paper, we review and address the impact of data security and privacy on the use of the MyHR system and its associated issues. We determine and analyse where privacy becomes an issue of using the MyHR system. Finally, we also present an appropriate method to protect the security and privacy of the MyHR system in Australia.
      PubDate: 2020-10-08
      DOI: 10.1007/s13755-020-00126-4
       
  • Automated detection of mild and multi-class diabetic eye diseases using
           deep learning

    • Abstract: Diabetic eye disease is a collection of ocular problems that affect patients with diabetes. Thus, timely screening enhances the chances of timely treatment and prevents permanent vision impairment. Retinal fundus images are a useful resource to diagnose retinal complications for ophthalmologists. However, manual detection can be laborious and time-consuming. Therefore, developing an automated diagnose system reduces the time and workload for ophthalmologists. Recently, the image classification using Deep Learning (DL) in between healthy or diseased retinal fundus image classification already achieved a state of the art performance. While the classification of mild and multi-class diseases remains an open challenge, therefore, this research aimed to build an automated classification system considering two scenarios: (i) mild multi-class diabetic eye disease (DED), and (ii) multi-class DED. Our model tested on various datasets, annotated by an opthalmologist. The experiment conducted employing the top two pretrained convolutional neural network (CNN) models on ImageNet. Furthermore, various performance improvement techniques were employed, i.e., fine-tune, optimization, and contrast enhancement. Maximum accuracy of 88.3% obtained on the VGG16 model for multi-class classification and 85.95% for mild multi-class classification.
      PubDate: 2020-10-08
      DOI: 10.1007/s13755-020-00125-5
       
  • Analysis of the global situation of COVID-19 research based on
           bibliometrics

    • Abstract: With the rapid global spread of the COVID-19 pandemic, researchers have contributed several important advances. The WHO and countries with severe outbreaks have developed diagnosis and treatment guidelines. Here, we analyze the current transformation and application of scientific research to global epidemic prevention and control. We described and analyzed current COVID-19 research from the perspectives of international cooperation, interdisciplinary cooperation, and research hotspots using a bibliometric clustering algorithm. Using the diagnosis and treatment guidelines of the WHO and the United States and China as examples, we evaluate the transformation of scientific results from basic research to applications. Scientific research results that have not yet been incorporated into these guidelines are summarized to encourage updates and improvements by applying scientific research to prevention and control. COVID-19 has fostered interdisciplinary cooperative research, and the current results are mainly focused on the origin, epidemiological characteristics, clinical research, and diagnosis and treatment methods for the virus. Due to the ongoing publication of new research, diagnosis and treatment guidelines are constantly improving. However, some research gaps still exist, and some results have not yet been incorporated into the guidelines. The current research is still in the preliminary exploratory stage, and some problems, such as weak international cooperation, unbalanced interdisciplinary cooperation, and the lack of coordination between research and applications, exist. Therefore, countries around the world must improve the International Public Health Emergency Management System and prepare for major public health emergencies in the future.
      PubDate: 2020-09-30
      DOI: 10.1007/s13755-020-00120-w
       
  • The investigation of multiresolution approaches for chest X-ray image
           based COVID-19 detection

    • Abstract: COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.
      PubDate: 2020-09-29
      DOI: 10.1007/s13755-020-00116-6
       
  • Estimation of infection density and epidemic size of COVID-19 using the
           back-calculation algorithm

    • Abstract: The novel coronavirus (COVID-19) is continuing its spread across the world, claiming more than 160,000 lives and sickening more than 2,400,000 people as of April 21, 2020. Early research has reported a basic reproduction number (R0) between 2.2 to 3.6, implying that the majority of the population is at risk of infection if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown, as a significant proportion of infected individuals only exhibit mild symptoms or are even asymptomatic. A timely assessment of the evolving epidemic size is crucial for resource allocation and triage decisions. In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID-19 infected persons in China in and outside the Hubei province. We estimate the infection density among infected and show that the drastic control measures enforced throughout China following the lockdown of Wuhan City effectively slowed down the spread of the disease in two weeks. We also investigate the COVID-19 epidemic size in South Korea and find a similar effect of its “test, trace, isolate, and treat” strategy. Our findings are expected to provide guidelines and enlightenment for surveillance and control activities of COVID-19 in other countries around the world.
      PubDate: 2020-09-28
      DOI: 10.1007/s13755-020-00122-8
       
  • PDCOVIDNet: a parallel-dilated convolutional neural network architecture
           for detecting COVID-19 from chest X-ray images

    • Abstract: The COVID-19 pandemic continues to severely undermine the prosperity of the global health system. To combat this pandemic, effective screening techniques for infected patients are indispensable. There is no doubt that the use of chest X-ray images for radiological assessment is one of the essential screening techniques. Some of the early studies revealed that the patient’s chest X-ray images showed abnormalities, which is natural for patients infected with COVID-19. In this paper, we proposed a parallel-dilated convolutional neural network (CNN) based COVID-19 detection system from chest X-ray images, named as Parallel-Dilated COVIDNet (PDCOVIDNet). First, the publicly available chest X-ray collection fully preloaded and enhanced, and then classified by the proposed method. Differing convolution dilation rate in a parallel form demonstrates the proof-of-principle for using PDCOVIDNet to extract radiological features for COVID-19 detection. Accordingly, we have assisted our method with two visualization methods, which are specifically designed to increase understanding of the key components associated with COVID-19 infection. Both visualization methods compute gradients for a given image category related to feature maps of the last convolutional layer to create a class-discriminative region. In our experiment, we used a total of 2905 chest X-ray images, comprising three cases (such as COVID-19, normal, and viral pneumonia), and empirical evaluations revealed that the proposed method extracted more significant features expeditiously related to suspected disease. The experimental results demonstrate that our proposed method significantly improves performance metrics: the accuracy, precision, recall and F1 scores reach \(96.58\%\) , \(96.58\%\) , \(96.59\%\) and \(96.58\%\) , respectively, which is comparable or enhanced compared with the state-of-the-art methods. We believe that our contribution can support resistance to COVID-19, and will adopt for COVID-19 screening in AI-based systems.
      PubDate: 2020-09-21
      DOI: 10.1007/s13755-020-00119-3
       
  • Variability analysis of epileptic EEG using the maximal overlap discrete
           wavelet transform

    • Abstract: Purpose To determine if there is a difference in the wavelet variances of seizure and non-seizure channels in the EEG of an epileptic subject. Methods A six-level decomposition was applied using the Maximal Overlap Discrete Wavelet Transform (MODWT). The wavelet variance and 95% CIs were calculated for each level of the decomposition. The number of changes in variance for each level were found using a change-point detection method of Whitcher. The Kruskal–Wallis test was used to determine if there were differences in the median number of change points within channels and across frequency bands (levels). Results No distinctive pattern was found for the wavelet variances to differentiate the seizure and non-seizure channels. The seizure channels tended to have lower variances for each level and overall, but this pattern only held for one of the three seizure channels (RAST4). The median number of change points did not differ between the seizure and non-seizure channels either within each channel or across the frequency bands. Conclusion The use of the MODWT in examining the variances and changes in variance did not show specific patterns which differentiate between seizure and non-seizure channels.
      PubDate: 2020-09-15
      DOI: 10.1007/s13755-020-00118-4
       
  • A semantic trajectory data warehouse for improving nursing productivity

    • Abstract: A Trajectory Data Warehouse is a central repository of large amount of data focusing on moving objects, which have been collected and integrated from multiple sources with spatial and temporal dimensions as the main metrics of analysis. By adding semantic-related contextual information, it is converted to a Semantic Trajectory Data Warehouse. It transforms raw trajectories to valuable information that can be utilized for decision-making purposes in ubiquitous applications. Human recourses management is a domain that may benefit significantly from semantic trajectory data warehouses. In particular, employees working shifts can be considered as trajectories. In this work, standard data warehousing tools are used to store data about nursing personnel shifts as trajectories of moving persons. The conceptual and logical modelling of the semantic trajectory data warehouse is developed. The objective is the observation, management and scheduling of nurses’ shifts data by the computation of OLAP operations over them. A prototype implementation has also been realized to illustrate the functionality of the proposed model. The produced results prove the efficiency in improving nursing productivity.
      PubDate: 2020-08-29
      DOI: 10.1007/s13755-020-00117-5
       
  • The correlation of everyday cognition test scores and the progression of
           Alzheimer’s disease: a data analytics study

    • Abstract: The process of diagnosing dementia conditions, especially Alzheimer’s disease, and the cognitive tests that are involved in this process, are important areas of study. Everyday Cognition (ECog) is one test that can be used as part of Alzheimer’s disease diagnosis to measure cognitive decline in different areas. In this study, we investigate two versions of the ECog test: the study partner reported version (ECogSP), and the patient reported version (ECogPT). We compare these, using statistical analysis and machine learning techniques, to create classification models to demonstrate the progression in ECog scores over time by using the Alzheimer’s Disease Neuroimaging Initiative longitudinal data repository (ADNI); participants are classed with having normal cognition, mild cognitive impairment, or Alzheimer’s disease. We found that participants who are diagnosed with Alzheimer’s disease at baseline, or during a subsequent visit, tend to self-report consistent ECogPT scores over time indicating no change in cognitive ability. However, study partners tend to report higher and increasing ECogSP scores on behalf of participants in the same diagnosis category; this would indicate a degradation in the participant’s cognitive ability over time, consistent with the progress of Alzheimer’s disease.
      PubDate: 2020-07-23
      DOI: 10.1007/s13755-020-00114-8
       
  • An embedded novel compact feature profile image in speech signal for
           teledermoscopy system

    • Abstract: Background and objectives Teledermoscopy is a promising telemedicine service for remote diagnosis and treatment of skin diseases using dermoscopy images. It requires high quality transmission services, efficient utilization of channel bandwidth, effective storage, and security. Thus, this work develops an improved teledermoscopy system that guarantees the efficient and secure transmission of the dermoscopy images. It proposed a novel feature-based secure diagnostic system that supports the automated classification of malignant melanoma and benign nevus at the receiver side (i.e. medical facility). Methods To overcome the transmission of the original dermoscopy images having large size, a novel representation of the dermoscopy images is proposed, namely the compact feature profile (CFP). The proposed CFP represents the dermoscopy image only using its significant features. For security purpose, the CFP is embedded as a watermark in a speech signal using singular value decomposition (SVD) watermarking at the transmitter. Then, the de-embedding/reconstruction process is performed at the receiver end using a proposed modified SVD technique. Finally, the extracted CFP is fed into a classifier for diagnosis at the receiver. To evaluate the robustness of the proposed system, an additive white Gaussian noise (AWGN) attack was employed during the transmission process. To improve the immunity against the AWGN attack, a novel speech signal weight factor is proposed at the watermarking process. Moreover, a compensation factor is calculated at the training phase to compensate the effect of the channel AWGN attack at the receiver. In addition, the superior transform domain and embedding positions of the CFP in the speech signal were studied. Results The experimental results established that the proposed CFP diagnostic system achieved high classification accuracy, sensitivity, specificity, and F-measure for classifying the two skin cancer classes with the presence of signal-to-noise ratio (SNR) ranging from 10 to 25 dB. Conclusion This work established that the newly proposed CFP watermarked in speech signal using the DWT-based modified SVD followed by single-level decomposition Db1 with hard thresholding wavelet denoising achieved efficient diagnostic teledermoscopy system.
      PubDate: 2020-06-25
      DOI: 10.1007/s13755-020-00113-9
       
  • RSMOTE: improving classification performance over imbalanced medical
           datasets

    • Abstract: Introduction Medical diagnosis is a crucial step for patient treatment. However, diagnosis is prone to bias due to imbalanced datasets. To overcome the imbalanced dataset problem, simple minority oversampling technique (SMOTE) was proposed that can generate new synthetic samples at data level to create the balance between minority and majority classes. However, the synthetic samples are generated on a random basis which causes class mixture problem; thus, resulting in deteriorating the classification performance and biased diagnosis. Purpose In order to overcome the SMOTE shortcomings, some modified methods were proposed that try to generate synthetic samples along the line segment of selected minority samples. Most of these methods adopt one of the two policies for selecting minority samples to generate synthetic samples: borderline region sampling or safe region sampling. However, they both suffer from over-generalisation problem. We propose a modified SMOTE-based resampling method called RSMOTE to alleviate the medical imbalanced dataset problem. We provide an in-depth analysis and verify the performance of RSMOTE over imbalanced medical datasets. Methods In this paper, the proposed RSMOTE divides the minority sample domain into four regions (normal, semi-normal, semi-critical, and critical) based on the minority sample density analysis. RSMOTE discovers the minority sample region globally and applies the resampling near a specific group of samples. Results Our analysis and experiments verify that if synthetic samples are generated in the regions with high minority sample density, classification performance will be improved due to low risk of class mixture. Unlike some safe region methods, RSMOTE decides the region of minority samples on a global basis, thus removing the over-generalisation problem. Classic and additional evaluation metrics are considered to measure the effectiveness of the modified method: Recall, FP Rate, Precision, F-Measure, ROC area, and Average Aggregated Metric. We carried out experiments over various imbalanced medical datasets. Conclusion Based on the minority sample density analysis, we propose RSMOTE method that divides the minority sample domain into four regions. The proposed RSMOTE includes four re-sampling methods that each of them carries out resampling on a specific region. According to the experimental results, resampling on the regions with high minority sample density obtained better results while those with lower minority sample density got the inferior results. Thus, we conclude that the RSMOTE is a more flexible resampling method for the imbalanced medical datasets that is capable of generating samples with various minority sample densities.
      PubDate: 2020-06-12
      DOI: 10.1007/s13755-020-00112-w
       
  • Synthesis of fracture radiographs with deep neural networks

    • Abstract: Purpose We describe a machine learning system for converting diagrams of fractures into realistic X-ray images. We further present a method for iterative, human-guided refinement of the generated images and show that the resulting synthetic images can be used during training to increase the accuracy of deep classifiers on clinically meaningful subsets of fracture X-rays. Methods A neural network was trained to reconstruct images from programmatically created line drawings of those images. The images were then further refined with an optimization-based technique. Ten physicians were recruited into a study to assess the realism of synthetic radiographs created by the neural network. They were presented with mixed sets of real and synthetic images and asked to identify which images were synthetic. Two classifiers were trained to detect humeral shaft fractures: one only on true fracture images, and one on both true and synthetic images. Results Physicians were 49.63% accurate in identifying whether images were synthetic or real. This is close to what would be expected by pure chance (i.e. random guessing). A classifier trained only on real images detected fractures with 67.21% sensitivity when no fracture fixation hardware was present. A classifier trained on both real images and synthetic images was 75.54% sensitive. Conclusion Our method generates X-rays realistic enough to be indistinguishable from real X-rays. We also show that synthetic images generated using this method can be used to increase the accuracy of deep classifiers on clinically meaningful subsets of fracture X-rays.
      PubDate: 2020-05-30
      DOI: 10.1007/s13755-020-00111-x
       
  • Local feature descriptors based ECG beat classification

    • Abstract: ECG beat type analysis is important in the detection of various heart diseases. The ECG beats give useful information about the status of the monitored heart condition. Up to now, various artificial intelligence-based methods have been proposed for ECG based heart failure detection. These methods were generally based on either time or frequency domain signal processing routines. In this study, we propose a different approach for ECG beat classification. The proposed approach is based on image processing. Thus, the initial step of the proposed work is converting the ECG beat signals to the ECG beat images. To do that, the ECG beat snapshots are initially saved as ECG beat images and then local feature descriptors are considered for feature extraction from ECG beat images. Eight local feature descriptors namely Local Binary Patterns, Frequency Decoded LBP, Quaternionic Local Ranking Binary Pattern, Binary Gabor Pattern, Local Phase Quantization, Binarized Statistical Image Features, CENsus TRansform hISTogram and Pyramid Histogram of Oriented Gradients are considered for feature extraction. The Support Vector Machines (SVM) classifier is used in the classification stage of the study. Linear, Quadratic, Cubic and Gaussian kernel functions are used in the SVM classifier. Five types of ECG beats from the MIT-BIH arrhythmia dataset are considered in experiments and the classification accuracy is used for performance measure. To construct a balanced training and test sets, 5000 and 10,000 ECG beat samples are randomly selected and are used in experiments in tenfold cross-validation fashion. The obtained results show that the proposed method is quite efficient where the calculated accuracy score is 99.9% and the comparisons with the state-of-the-art method show that the proposed method outperforms other methods.
      PubDate: 2020-05-02
      DOI: 10.1007/s13755-020-00110-y
       
  • A stacked LSTM for atrial fibrillation prediction based on multivariate
           ECGs

    • Abstract: Atrial fibrillation (AF) is an irregular and rapid heart rate that can increase the risk of various heart-related complications, such as the stroke and the heart failure. Electrocardiography (ECG) is widely used to monitor the health of heart disease patients. It can dramatically improve the health and the survival rate of heart disease patients by accurately predicting the AFs in an ECG. Most of the existing researches focus on the AF detection, but few of them explore the AF prediction. In this paper, we develop a recurrent neural network (RNN) composed of stacked LSTMs for AF prediction, which called SLAP. This model can effectively avoid the gradient explosion and gradient explosion of ordinary RNN and learn the features better. We conduct comprehensive experiments based on two public datasets. Our experiment results show 92% accuracy and 92% f-score of the AF prediction, which are better than the state-of-the-art AF detection architectures like the RNN and the LSTM.
      PubDate: 2020-04-21
      DOI: 10.1007/s13755-020-00103-x
       
  • Keyword extraction and structuralization of medical reports

    • Abstract: Purpose In recent years, patients usually accept more accurate and detailed examinations because of the rapid advances in medical technology. Many of the examination reports are not represented in numerical data, but text documents written by the medical examiners based on the observations from the instruments and biochemical tests. If the above-mentioned unstructured data can be organized as a report in a structured form, it will help doctors to understand a patient's status of the various examinations more efficiently. Besides, further association analysis on the structuralized data can be performed to identify potential factors that affect a disease. Methods In this paper, from the pathology examination reports of renal diseases, we applied the POS tagging results of natural language analysis to automatically extract the keyword phrases. Then a medical dictionary for various examination items in an examination report is established, which is used as the basic information for retrieving the terms to construct a structured form of the report. Moreover, a topical probability modeling method is applied to automatically discover the candidate keyword phrases of the examination items from the reports. Finally, a system is implemented to generate the structured form for the various examination items in a report according to the constructed medical dictionary. Results and conclusion The results of the experiments showed that the methods proposed in this paper can effectively construct a structural form of examination reports. Furthermore, the keywords of the popular examination items can be extracted correctly. The above techniques will help automatic processing and analysis of medical text reports.
      PubDate: 2020-04-03
      DOI: 10.1007/s13755-020-00108-6
       
 
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