Abstract
Deep learning has become one of the trendiest fields in recent years. Nonetheless, it is an appealing and hard undertaking to do tasks related to computer vision, natural language processing, speech recognition, bioinformatics, and smart health care. Acquiring information and significant knowledge from complex, high-dimensional, and heterogeneous biomedical data sources remains a critical test in changing health care. Different kinds of data have been arising in present-day biomedical exploration, including electronic health records, imaging, sensor data, and information, which are perplexing, heterogeneous, inadequately explained and for the most part unstructured. Conventional data mining and machine learning approaches regularly need to initially perform feature designing to acquire robust and more vigorous features from those data and afterward build prediction or clustering models on top of them. There are several severe difficulties on the two stages in the case of complex data and lacking adequate domain information. Based on machine learning, medical IoT devices, detection and diagnosis through imaging, automated surgeries and other several techniques have been in use in real-world and some are still under development. A few potential issues like security, QoS improvement, and arrangement demonstrate the vital piece of deep learning. We reveal working principle, deep learning for health care including images and text, smart devices and privacy issues in health care data.
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References
Xu, R., Li, L., Wang, Q.: dRiskKB: a large-scale disease-disease risk relationship knowledge base constructed from biomedical text. BMC Bioinf. 15, 105 (2014)
Chen, Y., Li, L., Zhang, G.-Q., et al.: Phenome-driven disease genetics prediction toward drug discovery. Bioinformatics 31, i276-83 (2015)
Wang, B., Mezlini, A.M., Demir, F., et al.: Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333–337 (2014)
Tatonetti, N.P., Ye, P.P., Daneshjou, R., et al.: Data-driven prediction of drug effects and interactions. Sci. Transl. Med. 4, 125ra31 (2012)
Li, L., Cheng, W.-Y., Glicksberg, B.S., et al.: Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci. Transl. Med. 7, 311ra174 (2015)
Libbrecht, M.W., Noble, W.S.: Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321–32 (2015)
Wang, F., Zhang, P., Wang, X., et al.: Clinical risk prediction by exploring high-order feature correlations. AMIA Annual Symp. 2014, 1170–9 (2014)
SNOMED CT. https://www.nlm.nih.gov/healthit/snomedct/index.html
Unified Medical Language System (UMLS). https://www.nlm.nih.gov/research/umls/
ICD-9 Code. https://www.cms.gov/medicare-coverage-database/staticpages/icd-9-code-lookup.aspx
Mohan, A., Blough, D.M., Kurc, T., et al.: Detection of conflicts and inconsistencies in taxonomy-based authorization policies. In: 2011 IEEE International Conference on Bioinformatics and Biomedicine, Atlanta, GA, USA, pp. 590–594 (2011)
Demner-Fushman, D., Chapman, W.W., McDonald, C.J.: What can natural language processing do for clinical decision support? J. Biomed. Inf. 42(5), 760–772 (2009)
Bedi, G., Carrillo, F., Cecchi, G.A., Slezak, D.F., Sigman, M., Mota, N.B., et al.: Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophrenia 1(1), 15030 (2015)
Chang, E.K., Christine, Y.Y., Clarke, R., Hackbarth, A., Sanders, T., Esrailian, E., et al.: Defining a patient population with cirrhosis: an automated algorithm with natural language processing. J. Clin. Gastroenterol. 50(10), 889–894 (2016)
Osborne, J.D., Wyatt, M., Westfall, A.O., Willig, J., Bethard, S., Gordon, G.: Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning. J. Am. Med. Inf. Assoc. 23(6), 1077–1084 (2016)
Cai, L., Gao, J., Zhao, D.: A review of the application of deep learning in medical image classification and segmentation. Annals Trans. Med. 8, 713–713 (2020). https://doi.org/10.21037/atm.2020.02.44
Greenspan, H., Van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imag. 35(5), 1153–1159 (2016)
Giger, M.L.: Machine learning in medical imaging. J. Am. College Radiol. 15(3), 512–520 (2018)
Sherwani, M.K., Zaffino, P., Bruno, P., Spadea, M.F., Calimeri, F.: Evaluating the impact of training loss on MR to synthetic CT conversion. In: Nicosia, G., et al. (eds.) Machine Learning,Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science, vol. 12565. Springer, Cham (2020)
Islam, J., Zhang, Y.: Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Inf. 5(2) (2018)
Gan, W., et al.: Privacy preserving utility mining: a survey. In: 2018 IEEE International Conference on Big Data (Big Data). IEEE (2018)
Chamikara, M.A.P., et al.: An efficient and scalable privacy preserving algorithm for big data and data streams. Comput. Sec. 87, 101570 (2019)
Canbay, Y., Vural, Y., Sagiroglu, S.: Privacy preserving big data publishing. In: International Congress on Big Data. Deep Learning and Fighting Cyber Terrorism (IBIGDELFT). IEEE (2018)
Dash, Sabyasachi, et al.: Big data in healthcare: management, analysis and future prospects. J. Big Data 6.1 (2019)
Tran, H.-Y., Jiankun, Hu.: Privacy-preserving big data analytics a comprehensive survey. J. Parallel Distrib. Comput. 134, 207–218 (2019)
Dai, W., et al.: Privacy preserving federated big data analysis. In: Guide to Big Data Applications, pp. 49–82. Springer, Cham (2018)
Hameed, S.S., et al.: A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches. PeerJ Comput. Sci. (2021)
Tobore, I., Li, et al.: Deep learning intervention for health care challenges: some biomedical domain considerations. JMIR mHealth and uHealth (2019)
Obinikpo, A.A., et al.: Big sensed data meets deep learning for smarter health care in smart cities. J. Sens. Actuator Netw. (2019)
Holzinger, A., et al.: From smart health to smart hospitals. Springer book Smart health (2020)
Holzinger, A., et al.: Smart health: open problems and future challenges. Springer book series (2015)
Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9(1), 12495 (2019). https://doi.org/10.1038/s41598-019-48995-4. PMID: 31467326; PMCID: PMC6715802
Rajkomar, A., Oren, E., Chen, K., et al.: Scalable and accurate deep learning with electronic health records. npj Digital Med. 1, 18 (2018). https://doi.org/10.1038/s41746-018-0029-1
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Sherwani, M.K., Aziz, A., Calimeri, F. (2022). Role of Deep Learning for Smart Health Care. In: Lahby, M., Al-Fuqaha, A., Maleh, Y. (eds) Computational Intelligence Techniques for Green Smart Cities. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-96429-0_8
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