A review of big data applications of physiological signal data.

作者: Christina Orphanidou

DOI: 10.1007/S12551-018-0495-3

关键词:

摘要: The proliferation of smart physiological signal monitoring sensors, combined with the advancement telemetry and intelligent communication systems, has led to an explosion in healthcare data past few years. Additionally, access cheaper more effective power storage mechanisms significantly increased availability for development big applications. Big applications are concerned analysis datasets which too big, fast, complex providers process interpret existing tools. driver such systems is continuing effort making services efficient sustainable. In this paper, we provide a review current utilize waveforms or derived measurements order medical decision support, often real time, clinical home environment. We focus mainly on developed continuous patient critical care discuss challenges that need be overcome these can incorporated into practice. Once overcome, have potential transform management hospital future.

参考文章(44)
Marco A. F. Pimentel, Thomas Brennan, Li-wei Lehman, Nicolas Kon Kam King, Beng-Ti Ang, Mengling Feng, Outcome Prediction for Patients with Traumatic Brain Injury with Dynamic Features from Intracranial Pressure and Arterial Blood Pressure Signals: A Gaussian Process Approach. Acta Neurochirurgica. ,vol. 122, pp. 85- 91 ,(2016) , 10.1007/978-3-319-22533-3_17
Christina Orphanidou, David Wong, Machine Learning Models for Multidimensional Clinical Data Handbook of Large-Scale Distributed Computing in Smart Healthcare. pp. 177- 216 ,(2017) , 10.1007/978-3-319-58280-1_8
Christina Orphanidou, Signal Quality Assessment in Physiological Monitoring Springer International Publishing. ,(2018) , 10.1007/978-3-319-68415-4
Venketesh Palanisamy, Ramkumar Thirunavukarasu, Implications of big data analytics in developing healthcare frameworks – A review Journal of King Saud University - Computer and Information Sciences. ,vol. 31, pp. 415- 425 ,(2019) , 10.1016/J.JKSUCI.2017.12.007
L. Nelson Sanchez-Pinto, Yuan Luo, Matthew M. Churpek, Big Data and Data Science in Critical Care Chest. ,vol. 154, pp. 1239- 1248 ,(2018) , 10.1016/J.CHEST.2018.04.037
Peter Szolovits, Rohit Joshi, Prognostic physiology: modeling patient severity in Intensive Care Units using radial domain folding. american medical informatics association annual symposium. ,vol. 2012, pp. 1276- 1283 ,(2012)
Ashwin Belle, Raghuram Thiagarajan, S. M. Reza Soroushmehr, Fatemeh Navidi, Daniel A. Beard, Kayvan Najarian, Big Data Analytics in Healthcare BioMed Research International. ,vol. 2015, pp. 370194- 370194 ,(2015) , 10.1155/2015/370194
Javier Andreu-Perez, Carmen C. Y. Poon, Robert D. Merrifield, Stephen T. C. Wong, Guang-Zhong Yang, Big Data for Health IEEE Journal of Biomedical and Health Informatics. ,vol. 19, pp. 1193- 1208 ,(2015) , 10.1109/JBHI.2015.2450362
Hugh Durrant-Whyte, Thomas C. Henderson, Multisensor Data Fusion Springer Handbook of Robotics, 2nd Ed.. pp. 867- 896 ,(2016) , 10.1007/978-3-319-32552-1_35
Alexander Roederer, James Weimer, Joseph DiMartino, Jacob Gutsche, Insup Lee, Robust monitoring of hypovolemia in intensive care patients using photoplethysmogram signals international conference of the ieee engineering in medicine and biology society. ,vol. 2015, pp. 1504- 1507 ,(2015) , 10.1109/EMBC.2015.7318656