Featured Articles

  • Remote Assessment of Cognitive Impairment Level based on Serious Mobile Game Performance: An Initial Proof of Concept
    VOL. 23, ISSUE 3

    Remote Assessment of Cognitive Impairment Level based on Serious Mobile Game Performance: An Initial Proof of Concept

    The number of individuals with permanent cognitive disabilities is increasing worldwide due to aging societies. It is important to longitudinally monitor changes in the cognitive functional and impairment levels of these individuals in order to evaluate the effectiveness of the prescribed intervention and enable individually-tailored therapeutic programs. In practice, different aspects of individuals’ cognition are assessed by clinically validated tools, such as the Mini Mental State Examination (MMSE). However, such a tool needs to be administered by trained clinical staff to achieve accuracy and reliability. This constraint serves as a major barrier that makes the frequent and longitudinal assessment difficult. 

  • VOL. 23, ISSUE 3

    Position Paper
    Deep Learning: Current and Emerging Applications in Medicine and Technology

    Machine learning is enabling researchers to analyze and understand increasingly complex physical and biological phenomena in traditional fields such as biology, medicine, and engineering and emerging fields like synthetic biology, automated chemical synthesis, and bio-manufacturing. These fields require new paradigms towards understanding increasingly complex data and converting such data into medical products and services for patients.

  • Special Issue on AI Enabled Connected Health Informatics (IEEE BHI 2018)
    VOL. 23, ISSUE 3

    Special Issue on AI Enabled Connected Health Informatics (IEEE BHI 2018)

    Special Issue on AI Enabled Connected Health Informatics (IEEE BHI 2018) articles in Vol. 23, Issue 3

  • Sensor Informatics
    VOL. 23, ISSUE 3

    Sensor Informatics

    Sensor Informatics articles in Vol. 23, Issue 3

  • Imaging Informatics
    VOL. 23, ISSUE 3

    Imaging Informatics

    Imaging Informatics articles in Vol. 23, Issue 3

  • VOL. 23, ISSUE 3

    Medical Informatics

    Medical Informatics articles in Vol. 23, Issue 3

  • Bioinformatics
    VOL. 23, ISSUE 3

    Bioinformatics

    Bioinformatics articles in Vol. 23, Issue 3

  • Public Health Informatics
    Vol. 23, Issue 3

    Public Health Informatics

    Public Health Informatics articles in Vol. 23, Issue 3

  • Position Paper Into the Wild: The Challenges of Physiological Stress Detection in Laboratory and Ambulatory Settings
    Vol. 23, Issue 2

    DermaKNet: Incorporating the knowledge of dermatologists to Convolutional Neural Networks for skin lesion diagnosis

    Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers working on sets of hand-crafted features, some of which modeled lesion aspects of special importance for dermatologists. Recently, the broad adoption of Convolutional Neural Networks(CNNs)in most computer vision tasks has brought about a great leap forward in terms of performance. Nevertheless, with this performance leap, the CNN-based Computer Aided Diagnosis (CAD) systems have also brought a notable reduction of the useful insights provided by hand-crafted features. This paper presents DermaKNet, a CAD system based on CNNs that incorporates specific subsystems modeling properties of skin lesions that are of special interest to dermatologists, aiming to improve the interpretability of its diagnosis. Our results prove that the incorporation of these subsystems not only improves the performance, but also enhances the diagnosis by providing more interpretable outputs.

  • Vol. 23, Issue 2

    Position Paper
    Into the Wild: The Challenges of Physiological Stress Detection in Laboratory and Ambulatory Settings

    Stress and mental health have become major concerns worldwide. Research has already extensively investigated physiological signals as quantitative and continuous markers of stress. In recent years the focus of the field has shifted from the laboratory to the ambulatory environment. We provide an overview of physiological stress detection in laboratory settings with a focus on identifying physiological sensing priorities, including electrocardiogram, skin conductance and electromyogram, and the most suitable machine learning techniques, of which the choice depends on the context of the application. Additionally, an overview is given of new challenges ahead to move towards the ambulant environment, including the influence of physical activity, lower signal quality due to motion artifacts, the lack of a stress reference and the subject-dependent nature of the physiological stress response. Finally, several recommendations for future research are listed, focusing on large scale, longitudinal trials across different population groups and just-in-time interventions to move towards disease prevention and interception.

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