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Overview of recent advances in Health care technology and its impact on health care delivery

Editor IJSMI


Recent advancement in technology such as Machine Learning (ML), Artificial intelligence(AI), Robotics, internet of things (IOT), Block Chain technologies, Big Data analytics, Cloud computing  Natural Language Processing, Mobile Applications is making a huge impact on the day to day lives of human beings. These technologies started helping us to save resources, time and cost and at the same time increase the accuracy and efficiency. Biomedical domain also started embracing these new technologies in the areas of diagnosis, surgery and therapeutics. These technologies also have applications in the areas of pattern recognition and expert systems. The paper provides an overview of recent advancement in technologies and its impact on the biomedical domain   


Machine Learning; Artificial intelligence; Robotics; Internet of Things; Block Chain; Technologies; Big Data Analytics

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Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media.

Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160, 3-24.

Cruz, J. A., & Wishart, D. S. (2006). Applications of machine learning in cancer prediction and prognosis. Cancer informatics, 2, 117693510600200030.

Nasrabadi, N. M. (2007). Pattern recognition and machine learning. Journal of electronic imaging, 16(4), 049901.

Foster, K. R., Koprowski, R., & Skufca, J. D. (2014). Machine learning, medical diagnosis, and biomedical engineering research-commentary. Biomedical engineering online, 13(1), 94.

Sajda, P. (2006). Machine learning for detection and diagnosis of disease. Annu. Rev. Biomed. Eng., 8, 537-565.

Walczak, S. (2018). Artificial neural networks. In Encyclopedia of Information Science and Technology, Fourth Edition (pp. 120-131). IGI Global.

Adankon, M. M., & Cheriet, M. (2009). Support vector machine. In Encyclopedia of biometrics (pp. 1303-1308). Springer US.

Murphy, K. P. (2006). Naive bayes classifiers. University of British Columbia, 18.

Goodman, K. E., Lessler, J., Cosgrove, S. E., Harris, A. D., Lautenbach, E., Han, J. H., ... & Tamma, P. D. (2016). A clinical decision tree to predict whether a bacteremic patient is infected with an extended-spectrum β-Lactamase–producing organism. Clinical Infectious Diseases, 63(7), 896-903.

Wang, L., Wang, Z., & Liu, S. (2016). An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm. Expert Systems with Applications, 43, 237-249.

Kubat, M. (2015). Artificial neural networks. In An Introduction to Machine Learning (pp. 91-111). Springer, Cham.

Namin, A. H., Leboeuf, K., Wu, H., & Ahmadi, M. (2009, June). Artificial neural networks activation function HDL coder. In Electro/Information Technology, 2009. eit'09. IEEE International Conference on (pp. 389-392). IEEE.

Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4), 303-314.

Jang, J. S., & Sun, C. T. (1993). Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE transactions on Neural Networks, 4(1), 156-159.

Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 807-814).

Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013). Artificial neural networks in medical diagnosis.

Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology, 49(11), 1225-1231.

Khan, J., Wei, J. S., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., ... & Meltzer, P. S. (2001). Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature medicine, 7(6), 673.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.

Bengio, Y., Goodfellow, I. J., & Courville, A. (2015). Deep learning. Nature, 521(7553), 436-444.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).

Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010). Recurrent neural network based language model. In Eleventh Annual Conference of the International Speech Communication Association.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

Hinton, G. E. (2009). Deep belief networks. Scholarpedia, 4(5), 5947.

Larry Hauser, Internet Encylopedia of Philosophy http://www.iep.utm.edu/art-inte/

Berner, E. S. (2007). Clinical decision support systems (Vol. 233). New York: Springer Science+ Business Media, LLC.

Shortliffe, E. (Ed.). (2012). Computer-based medical consultations: MYCIN (Vol. 2). Elsevier.

De la Rosa Algarın, A. (2011). Clinical Decision Support Systems in Biomedical Informatics and their Limitations.

Ravindranath, K. R. (2015, January). Clinical Decision Support System for heart diseases using Extended sub tree. In Pervasive Computing (ICPC), 2015 International Conference on (pp. 1-5). IEEE.

Xia, F., Yang, L. T., Wang, L., & Vinel, A. (2012). Internet of things. International Journal of Communication Systems, 25(9), 1101.

Hiremath, S., Yang, G., & Mankodiya, K. (2014, November). Wearable Internet of Things: Concept, architectural components and promises for person-centered healthcare. In Wireless Mobile Communication and Healthcare (Mobihealth), 2014 EAI 4th International Conference on (pp. 304-307). IEEE.

Islam, S. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K. S. (2015). The internet of things for health care: a comprehensive survey. IEEE Access, 3, 678-708.

Amendola, S., Lodato, R., Manzari, S., Occhiuzzi, C., & Marrocco, G. (2014). RFID technology for IoT-based personal healthcare in smart spaces. IEEE Internet of things journal, 1(2), 144-152.

Pilkington, M. (2016). 11 Blockchain technology: principles and applications. Research handbook on digital transformations, 225.

Mettler, M. (2016, September). Blockchain technology in healthcare: The revolution starts here. In e-Health Networking, Applications and Services (Healthcom), 2016 IEEE 18th International Conference on (pp. 1-3). IEEE.

Azaria, A., Ekblaw, A., Vieira, T., & Lippman, A. (2016, August). Medrec: Using blockchain for medical data access and permission management. In Open and Big Data (OBD), International Conference on (pp. 25-30). IEEE.

Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34.

Buhl, H. U., Röglinger, M., Moser, F., & Heidemann, J. (2013). Big data.

Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care. Jama, 309(13), 1351-1352.

Dimitrov, D. V. (2016). Medical internet of things and big data in healthcare. Healthcare informatics research, 22(3), 156-163.

Chowdhury, G. G. (2003). Natural language processing. Annual review of information science and technology, 37(1), 51-89.

Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations (pp. 55-60).

Aguirre, C. A., Coen, S., Maria, F., Hsu, W. H., & Rys, M. (2018). Towards Faster Annotation Interfaces for Learning to Filter in Information Extraction and Search.

Wang, X., Yang, C., & Guan, R. (2018). A comparative study for biomedical named entity recognition. International Journal of Machine Learning and Cybernetics, 9(3), 373-382.

Lewis, D. D., & Jones, K. S. (1996). Natural language processing for information retrieval. Communications of the ACM, 39(1), 92-101.

Martin, J. H., & Jurafsky, D. (2009). Speech and language processing: An introduction to natural language processing, computational linguisti

Kay, M., Santos, J., & Takane, M. (2011). mHealth: New horizons for health through mobile technologies. World Health Organization, 64(7), 66-71.

Kay, M., Santos, J., & Takane, M. (2011). mHealth: New horizons for health through mobile technologies. World Health Organization, 64(7), 66-71.

DOI: http://dx.doi.org/10.3000/ijsmi.v7i1.11

DOI (PDF): http://dx.doi.org/10.3000/ijsmi.v7i1.11.g56

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