Sensor, Signal, and Imaging Informatics: Big Data and Smart Health Technologies

Summary

Objectives: This synopsis presents a selection for the IMIA (International Medical Informatics Association) Yearbook 2014 of excellent research in the broad field of Sensor, Signal, and Imaging Informatics published in the year 2013, with a focus on Big Data and Smart Health Technologies

Methods: We performed a systematic initial selection and a double blind peer review process to find the best papers in this domain published in 2013, from the PubMed and Web of Science databases. A set of MeSH keywords provided by experts was used.

Results: Big Data are collections of large and complex datasets which have the potential to capture the whole variability of a study population. More and more innovative sensors are emerging, allowing to enrich these big databases. However they become more and more challenging to process (i.e. capture, store, search, share, transfer, exploit) because traditional tools are not adapted anymore.

Conclusions: This review shows that it is necessary not only to develop new tools specifically designed for Big Data, but also to evaluate their performance on such large datasets.

Keywords

Big data - smart health technologies

 

• References

• 1 Baig M, Gholamhosseini H. Smart Health Monitoring Systems: An Overview of Design and Modeling. J Med Syst 2013; 37 (02) 1-14.
• 2 Francis SV, Sasikala M. Automatic detection of abnormal breast thermograms using asymmetry analysis of texture features. J Med Eng Technol 2013; 37 (01) 17-21.
• 3 De Lorenzo D, Koseki Y, De Momi E, Chinzei K, Okamura AM. Coaxial Needle Insertion Assistant With Enhanced Force Feedback. IEEE Trans Biomed Eng 2013; Feb 60 (02) 379-89.
• 4 Heim L, Poole RJ, Warwick R, Poullis M. The concept of aortic replacement based on computational fluid dynamic analysis: patient-directed aortic replacement. Interact Cardiovasc Thorac Surg 2013; 16 (05) 583-7.
• 5 Güiza F, Van Eyck J, Meyfroidt G. Predictive data mining on monitoring data from the intensive care unit.. J Clin Monit Comput 2013; 27 (04) 449-53.
• 6 Clifton DA, Wong D, Clifton L, Wilson S, Way R, Pullinger R. et al. A Large-Scale Clinical Validation of an Integrated Monitoring System in the Emergency Department. IEEE J Biomed Heal Informatics 2013; 17 (04) 835-42.
• 7 Aguilar C, Westman E, Muehlboeck J-S, Mecocci P, Vellas B, Tsolaki M. et, al. Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment. Psychiatry Res 2013; 212 (02) 89-98.
• 8 Asman AJ, Chambless LB, Thompson RC, Land-man BA. Out-of-atlas likelihood estimation using multi-atlas segmentation. Med. Phys 2013; 40 (04) 043702.
• 9 Xie Y, Ho J, Vemuri BC. Multiple Atlas construction from a heterogeneous brain MR image collection. IEEE Trans Med Imaging 2013; 32 (03) 628-35.
• 10 Whitfield GA, Price P, Price GJ, Moore CJ. Automated delineation of radiotherapy volumes: are we going in the right direction?. Br J Radiol 2013; 86 1021 20110718.
• 11 Nie K, Chuang C, Kirby N, Braunstein S, Pouliot J. Site-specific deformable imaging registration algorithm selection using patient-based simulated deformations. Med Phys 2013; 40 (04) 041911.