32,093 research outputs found
What's unusual in online disease outbreak news?
Background: Accurate and timely detection of public health events of
international concern is necessary to help support risk assessment and response
and save lives. Novel event-based methods that use the World Wide Web as a
signal source offer potential to extend health surveillance into areas where
traditional indicator networks are lacking. In this paper we address the issue
of systematically evaluating online health news to support automatic alerting
using daily disease-country counts text mined from real world data using
BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration
detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance
against expert moderated ProMED-mail postings. Results: We report sensitivity,
specificity, positive predictive value (PPV), negative predictive value (NPV),
mean alerts/100 days and F1, at 95% confidence interval (CI) for 287
ProMED-mail postings on 18 outbreaks across 14 countries over a 366 day period.
Results indicate that W2 had the best F1 with a slight benefit for day of week
effect over C2. In drill down analysis we indicate issues arising from the
granular choice of country-level modeling, sudden drops in reporting due to day
of week effects and reporting bias. Automatic alerting has been implemented in
BioCaster available from http://born.nii.ac.jp. Conclusions: Online health news
alerts have the potential to enhance manual analytical methods by increasing
throughput, timeliness and detection rates. Systematic evaluation of health
news aberrations is necessary to push forward our understanding of the complex
relationship between news report volumes and case numbers and to select the
best performing features and algorithms
DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images
Copyright @ Skounakis et al.This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. AVAILABILITY: THE PLATFORM, A MANUAL AND TUTORIAL VIDEOS ARE AVAILABLE AT: http://biomodeling.ics.forth.gr. It is free to use under the GNU General Public License
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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