Toward unsupervised outbreak detection through visual perception of new patterns

<p>Abstract</p> <p>Background</p> <p>Statistical algorithms are routinely used to detect outbreaks of well-defined syndromes, such as influenza-like illness. These methods cannot be applied to the detection of emerging diseases for which no preexisting information is available.</p> <p>This paper presents a method aimed at facilitating the detection of outbreaks, when there is no a priori knowledge of the clinical presentation of cases.</p> <p>Methods</p> <p>The method uses a visual representation of the symptoms and diseases coded during a patient consultation according to the International Classification of Primary Care 2^ndversion (ICPC-2). The surveillance data are transformed into color-coded cells, ranging from white to red, reflecting the increasing frequency of observed signs. They are placed in a graphic reference frame mimicking body anatomy. Simple visual observation of color-change patterns over time, concerning a single code or a combination of codes, enables detection in the setting of interest.</p> <p>Results</p> <p>The method is demonstrated through retrospective analyses of two data sets: description of the patients referred to the hospital by their general practitioners (GPs) participating in the French Sentinel Network and description of patients directly consulting at a hospital emergency department (HED).</p> <p>Informative image color-change alert patterns emerged in both cases: the health consequences of the August 2003 heat wave were visualized with GPs' data (but passed unnoticed with conventional surveillance systems), and the flu epidemics, which are routinely detected by standard statistical techniques, were recognized visually with HED data.</p> <p>Conclusion</p> <p>Using human visual pattern-recognition capacities to detect the onset of unexpected health events implies a convenient image representation of epidemiological surveillance and well-trained "epidemiology watchers". Once these two conditions are met, one could imagine that the epidemiology watchers could signal epidemiological alerts, based on "image walls" presenting the local, regional and/or national surveillance patterns, with specialized field epidemiologists assigned to validate the signals detected.</p

Editorial

The nine papers in this special section address various aspects of the design, implementation, and application of biometric processes

Guest Editorial Introduction to the Special Section on Computational Intelligence in Medical Systems

RECENT technological advances in medicine have facil- itated the development of complex biomedical systems including sophisticated biomedical signal devices and instru- ments, medical imaging equipment, and computer-aided diag- nosis (CAD) tools enabling the better delivery of healthcare services. In parallel, computational intelligence, incorporat- ing neural computing, fuzzy systems, evolutionary computing, and more recently, rough sets, and autoimmune systems have emerged as promising tools for the development, application, and implementation of intelligent systems. In the last ten years, there has been a significant effort in the application of computational intelligent techniques in nu- merous biomedical systems. These cover applications in med- ical decision-making [1], biosignal analysis, and biomedical engineering at large [2], medical imaging [3]–[5], bioinformat- ics [6], [7], and others. All these systems underlie the impact of these technologies in the biomedical domain. The aim of this special issue is to focus on the most recent applications of computational intelligent systems in medicine. Papersinthisspecialissuecoverinnovativeapplicationsofcom- putational intelligence in the following physiological systems: skin, skeletal, muscular, central nervous, peripheral nervous, systems of special senses (eye), cardiovascular, respiratory, and reproductive. A total of 45 papers were submitted for this special issue that were reviewed by at least three reviewers. Following the recommendations of the guest editors and the Editor-in-Chief, 19 papers were accepted for publication. The accepted papers were organized under the topics: General, Computational Biol- ogy, Biosignal Analysis, and Medical Imaging, with two, three, seven, and seven papers in each topic, respectively. Some of these papers (10 in total) have been published earlier by mistake, unfortunately in previous issues of the IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN B OMEDICINE . All the accepted papers are briefly summarized in the following section. It is generally accepted that, nowadays, health services are facing a number of complex interacting and multifactorial challenges [8]. To address these issues from the information and communication technologies (ICTs) perspective, the World Health Assembly (WHA) adopted an eHealth Strategy for the World Health Organization (WHO) [9]. The resolution docu- mented that the use of ICT for health is one of the most rapidly growing application areas in health today. Moreover, it was pro- posed that automated or semiautomated systems that support decision-making in a clinical environment would be very useful for the better support of healthcare services. In parallel with the WHO activities, the European Commis- sion (EC) in 2004 adopted the eHealth action plan [10], as well as subsequent directives [11], that cover a wide spectrum of eHealth services, ranging from cross-border interoperability of electronic health record systems, to electronic prescriptions and health cards, to new information systems that are targeting to reduce waiting times and errors to facilitate a more harmonious and complementary European approach to eHealth. These initiatives, as stated in the Prague Declaration of the EU Member States, stress the need to keep the momentum so that the potential advantages of gradual deployment of ICT in the health sector are not compromised by barriers of legal, technical, economic, or any other nature. At the same time, it is considered crucial that the benefits of eHealth applications and services are further enhanced and properly distributed among all the relevant stakeholders, patients and healthcare professionals, society, and the economy. To facilitate the provision of better, and more efficient and effective eHealth services as documented before, sophisticated and advanced medical systems based on computational intel- ligence have to be developed. Although significant steps have been carried out in this direction in the last two decades, the need still exists that intelligent medical systems be developed, covering a wider spectrum of services, and most importantly, be thoroughly evaluated before their deployment in clinical prac- tice. The papers in this special issue cover a wide range of applications, demonstrating the promising potential of compu- tational intelligence in medical systems

Multimodal 3-D image registration of MRI-SPECT volume images

A method was developed to implement multimodal 3-D image registration using external markers, visible in both imaging modalities. The method was validated in a controlled mode using simulated images. The method was also successfully applied to real images of a given phantom acquired by MRI and SPECT