Featured Articles

  • Special Issue: IEEE BHI 2016

    THE IEEE International Conference on Biomedical and Health Informatics (BHI) is a special topic conference of IEEE Engineering in Medicine and Biology Society (IEEE-EMBS). BHI2016 was co-located with the Annual HIMSS Conference & Exhibition in Las Vegas, NV, USA, in February 24th-27th, 2016. The main theme of BHI2016 was “Integrative Informatics for Precision and Preventive Medicine.” Advancing health informatics has been identified as a grand challenge for engineering in the 21st century by the National Academy of Engineering. Maintaining and improving human health will require integrative and novel informatics solutions to better translate discovery into clinics, re-engineer care practices, and integrate big data of various health networks.

    BHI2016 provided a unique forum to showcase enabling technologies of computing, devices, imaging, sensors, and systems that optimize the acquisition, transmission, processing, storage, retrieval, visualization and analysis. In addition, it shared how integrative informatics solutions can be used in novel clinical applications to improve human health, and how the deployment of integrated bioinformatics, m-Health, e-Health, and tele-Health with enterprise IT can enable precision and preventive medicine.

  • Special Issue: Deep Learning for Biomedical and Health Informatics

    Special Issue: Deep Learning for Biomedical and Health Informatics

    This special issue aims to report the latest advances in the field of deep learning for biomedical and health informatics, and nine papers have been selected for this special issue which address both original algorithmic development and new applications of deep learning.

  • Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review

    Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review

    This paper provides a systematic review of current techniques for quantitative gait analysis and proposes key metrics for for qualifying gait features extracted from wearable sensors. It aims to highlight key advances in this rapidly evolving research field and outline potential future directions for both research and clinical applications

  • Special Issue: IEEE EMBC 2015

    Special Issue: IEEE EMBC 2015

    This special issue of the IEEE JOURNAL ON BIOMEDICAL AND HEALTH INFORMATICS (JBHI) presents full articles of the work presented at the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015), which was held in Milano, Italy, from August 25–29.

  • Special Issue: IEEE BSN 2015

    This special issue includes four papers that expand on work presented at the 12th BSN Conference, held in Cambridge, MA, USA, in June 2015, which included 85 oral and poster presentations. All four papers went through the rigorous review process of the IEEE Journal of Biomedical and Health Informatics (J-BHI).

  • Special Section: MobiHealth 2014, IEEE HealthCom 2014, and IEEE BHI 2014

    The aim of this special section is to present an overview of recent advances in sensing technologies, monitoring of patients,
    security and privacy of data transfer, provision of collaborative environments, data gathering and analysis from various sources, and predictive models, which all finally target the best strategy for patient monitoring and treatment.

  • Classification of Voluntary Cough Airflow Patterns for Prediction of Abnormal Spirometry

    In this work, we investigate the feasibility of predicting abnormal spirometry using measurements of the partial flow-volume curve generated during a voluntary cough. A variety of features were extracted from the cough airflow signals and fed to a support vector machine (SVM) classifier. Airflow features and SVM parameters were selected using a genetic algorithm, and accuracy assessed using repeated double cross-validation. This technique is easily performed and does not require expensive equipment. Based on these characteristics, and the results of this study, it has potential for becoming especially useful for mass screening or for testing subjects who are not able to perform conventional pulmonary function testing.

  • DBN-Extended: A Dynamic Bayesian Network Model Extended With Temporal Abstractions for Coronary Heart Disease Prognosis

    DBN-Extended: A Dynamic Bayesian Network Model Extended With Temporal Abstractions for Coronary Heart Disease Prognosis

    Dynamic Bayesian networks (DBNs) are temporal probabilistic graphical models that model temporal events and their causal and temporal dependencies. Temporal abstraction (TA) is a knowledge-based process that abstracts raw temporal data into higher level interval-based concepts. In this paper, we present an extended DBN model that integrates TA methods with DBNs applied for prognosis of the risk for coronary heart disease. More specifically, we demonstrate the derivation of TAs from data, which are used for building the network structure. We use machine learning algorithms to learn the parameters of the model through data. We apply the extended model to a longitudinal medical dataset and compare its performance to the performance of a DBN implemented without TAs. The results we obtain demonstrate the predictive accuracy of our model and the effectiveness of our proposed approach.

  • On the Influence of Confounding Factors in Multisite Brain Morphometry Studies of Developmental Pathologies: Application to Autism Spectrum Disorder

    On the Influence of Confounding Factors in Multisite Brain Morphometry Studies of Developmental Pathologies: Application to Autism Spectrum Disorder

    In this paper, we studied a dataset composed of 159 anatomical MR images pooled from three different scanners, including 75 ASD patients and 84 healthy controls. We quantitatively assessed the effects of the age, pathology and scanner factors on cortical thickness measurements. We observe that the effect size associated to the scanner factor is larger than the effect of pathology in almost every cortical region. Moreover, while the effect of age is consistent across scanners, the interaction between the age and scanner factors is important and significant in some specific cortical areas. Our results allow for a better understanding of the frequent discrepancies observed in the literature of ASD. We conclude that maximizing the consistency of image characteristics across scanners is mandatory for multi-site quantitative MRI studies in the context of neuro-developmental pathologies such as ASD.

  • Modeling the Interplay Between Tumor Volume Regression and Oxygenation in Uterine Cervical Cancer During Radiotherapy Treatment

    A novel modeling approach of tumor growth and response to radiotherapy is presented and tested on cervical cancer patient data. A patient-specific mathematical model is developed to predict the evolution of cancer volume at a macroscopic scale, during fractionated external radiotherapy. The model provides estimates of the re-growth of the active portion of the tumor along with their impairment due to irradiation using the linear-quadratic model. At the same time, it accounts for the necrotic portion dynamics by means of an exponential decay to mimic the dead-cell reabsorption.

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