Current Pharmacogenomics and Personalized Medicine

Current Pharmacogenomics and Personalized Medicine

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ISSN (Print): 1875-6921
ISSN (Online): 1875-6913

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Review Article

Exploring Medicine and Health Data Security: Key Insights and Best Practices

Author(s): Vishnu Mittal, Anjali Sharma*orcid of author and Devkant Sharma

Volume 23, 2026

Published on: 06 October, 2025

Article ID: e18756921397512

Pages: 20

DOI: 10.2174/0118756921397512250924105859

Price: $65

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Abstract

Introduction: With the exponential growth of digital health information systems, ensuring secure health data analytics has become critical for enhancing healthcare delivery, protecting patient privacy, and fostering innovation. The rise in privacy breaches and unauthorized data access has amplified the need for robust data security mechanisms during the health data analytics process.

Methods: This study employed a qualitative research methodology, incorporating multiple case studies from various healthcare organizations. Through in-depth interviews and document analysis, the study explored real-world challenges and strategies implemented to secure health data during analytics.

Results: Key findings revealed that while organizations face common challenges such as data integration, privacy concerns, and compliance with regulatory standards, the successful implementation of security practices like end-to-end encryption, strict access control, anonymization techniques and employee training can significantly mitigate risks. The comparative analysis across institutions highlighted recurring themes and effective practices that enhance data protection.

Discussion: The case studies demonstrate that secure health data analytics is achievable through a proactive, multi-layered approach. Integration of advanced cybersecurity protocols with existing health information systems not only prevents breaches but also builds public trust. Limitations of the study include the small sample size and organizational diversity, which may limit generalizability.

Conclusion: By synthesizing lessons learned and outlining best practices, this study provides a valuable reference for healthcare professionals, researchers, and policymakers. Adopting these measures enables stakeholders to securely leverage health data for insightful analytics, thereby advancing healthcare outcomes while maintaining patient confidentiality.

Keywords: Healthcare industry, cybersecurity, end-to-end encryption, organizational diversity, health data security, health information systems.

[1]
Zhang, Q.; Yang, L.T.; Chen, Z.; Li, P. A survey on deep learning for big data. Inf. Fusion, 2018, 42, 146-157.
[http://dx.doi.org/10.1016/j.inffus.2017.10.006]
[2]
Pavel, M.S.; Chakrabarty, S.; Gow, J. Cost of illness for outpatients attending public and private hospitals in Bangladesh. Int. J. Equity Health, 2016, 15(1), 167.
[http://dx.doi.org/10.1186/s12939-016-0458-x] [PMID: 27724955]
[3]
Aljaaf, A.J.; Ibrahim, M.; Al-Ouqaili, M.T.; Al-Jumeily, D.; Mustafina, J.; Al-Rawi, A.; Rasheed, M.M. Perspectives on industry 4.0 awareness among undergraduate IT students in IRAQ: University of Anbar as a case study. 2023 16th International Conference on Developments in eSystems Engineering (DeSE), Istanbul, Turkiye, 20 December 2023, pp. 777-781.
[http://dx.doi.org/10.1109/DeSE60595.2023.10468685]
[4]
Ali, M.; Gia, T.N.; Taha, A.; Rahmani, A.M.; Westerlund, T.; Liljeberg, P. Autonomous patient/home health monitoring powered by energy harvesting; IEEE GLOBECOM, 2017, pp. 1-7.
[http://dx.doi.org/10.1109/GLOCOM.2017.8253946]
[5]
S Al-Ouqaili, M.T.; Al-Ani, S.K.; Alaany, R.; Al-Qaisi, M.N. Detection of partial and/or complete Y chromosome microdeletions of azoospermia factor a (AZFa) sub-region in infertile Iraqi patients with azoospermia and severe oligozoospermia. J. Clin. Lab. Anal., 2022, 36(3), e24272.
[http://dx.doi.org/10.1002/jcla.24272] [PMID: 35122324]
[6]
Wicks, P.; Mack Thorley, E.; Simacek, K.; Curran, C.; Emmas, C. Scaling PatientsLikeMe via a “generalized platform” for members with chronic illness: Web-based survey study of benefits arising. J. Med. Internet Res., 2018, 20(5), e175.
[http://dx.doi.org/10.2196/jmir.9909] [PMID: 29735472]
[7]
Schmarzo, B. Big data: Understanding how data powers big business; Wiley: Indianapolis, 2013.
[8]
Wamba, S.F.; Gunasekaran, A.; Akter, S.; Ren, S.J.; Dubey, R.; Childe, S.J. Big data analytics and firm performance: Effects of dynamic capabilities. J. Bus. Res., 2017, 70, 356-365.
[http://dx.doi.org/10.1016/j.jbusres.2016.08.009]
[9]
Wang, Y.; Kung, L.; Wang, W.Y.C.; Cegielski, C.G. An integrated big data analytics-enabled transformation model: Application to health care. Inf. Manage., 2018, 55(1), 64-79.
[http://dx.doi.org/10.1016/j.im.2017.04.001]
[10]
Ristevski, B.; Chen, M. Big data analytics in medicine and healthcare. J. Integr. Bioinform., 2018, 15(3), 20170030.
[http://dx.doi.org/10.1515/jib-2017-0030] [PMID: 29746254]
[11]
Raghupathi, W.; Raghupathi, V. An overview of health analytics. J. Health Med. Inform., 2013, 4, 132.
[12]
Johnson, F.; Garza, S.; Gutierrez, K. Research on the use of voice to text applications for professional writing. IEEE International Professional Communication Conference, Austin, TX, USA, 05 October 2016, pp.1-6.
[http://dx.doi.org/10.1109/IPCC.2016.7740532]
[13]
Mahmud, MS; Wang, H; Fang, H SensoRing: An integrated wearable system for continuous measurement of physiological biomarkers. 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 24 May 2018, pp. 1-7.
[http://dx.doi.org/10.1109/ICC.2018.8423001]
[14]
Gunasekara, P.; Wijayakulasooriya, J.V.; Dharmagunawardhana, H.A.C. Image texture analysis using deep neural networks. IEEE International Conference on Industrial and Information Systems, Peradeniya, Sri Lanka, 16 December 2017, pp. 1-5.
[http://dx.doi.org/10.1109/ICIINFS.2017.8300362]
[15]
Neyja, M.; Mumtaz, S.; Huq, K.M.S.; Busari, S.A.; Rodriguez, J.; Zhou, Z. An IoT-based e-health monitoring system using ECG signal; IEEE GLOBECOM, 2017, pp. 1-6.
[http://dx.doi.org/10.1109/GLOCOM.2017.8255023]
[16]
Singh, N.; Singh, S. Object classification to analyze medical imaging data using deep learning; Int Conf Innov Inf Embed Commun Syst, 2018, pp. 1-4.
[17]
Kondo, T.; Ueno, J.; Takao, S. Medical image analysis of MRI brain images by deep RBF GMDH-type neural network using principal component-regression analysis. IAI 4th International Congress on Advanced Applied Informatics, Okayama, Japan, 16 July 2015, pp. 586-592
[http://dx.doi.org/10.1109/IIAI-AAI.2015.249.]
[18]
Shichijo, S.; Nomura, S.; Aoyama, K.; Nishikawa, Y.; Miura, M.; Shinagawa, T.; Takiyama, H.; Tanimoto, T.; Ishihara, S.; Matsuo, K.; Tada, T. Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images. EBioMedicine, 2017, 25, 106-111.
[http://dx.doi.org/10.1016/j.ebiom.2017.10.014] [PMID: 29056541]
[19]
Castro, E.M.; Van Regenmortel, T.; Vanhaecht, K.; Sermeus, W.; Van Hecke, A. Patient empowerment, patient participation and patient-centeredness in hospital care: A concept analysis based on a literature review. Patient Educ. Couns., 2016, 99(12), 1923-1939.
[http://dx.doi.org/10.1016/j.pec.2016.07.026] [PMID: 27450481]
[20]
Esteva, A.; Kuprel, B.; Novoa, R.A.; Ko, J.; Swetter, S.M.; Blau, H.M.; Thrun, S. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542(7639), 115-118.
[http://dx.doi.org/10.1038/nature21056] [PMID: 28117445]
[21]
Bibault, J.E.; Giraud, P.; Burgun, A. Big Data and machine learning in radiation oncology: State of the art and future prospects. Cancer Lett., 2016, 382(1), 110-117.
[http://dx.doi.org/10.1016/j.canlet.2016.05.033] [PMID: 27241666]
[22]
Kumari, V.A.; Chitra, R. Classification of diabetes disease using support vector machine. Int. J. Eng. Res. Appl., 2013, 3(2), 1797-1801.
[23]
Ba-alwi, F.M.; Hintaya, H.M. Comparative study for analysis of the prognostic in hepatitis data: Data mining approach. Int. J. Sci. Eng. Res., 2013, 4(8), 680-685.
[24]
Fathima, A.S.; Manimeglai, D. Predictive analysis for the arbovirus-dengue using SVM classification. Int J Eng Technol., 2012, 2(3), 521-527.
[25]
Tarmizi, N.A.; Jamaluddin, F. Malaysia dengue outbreak detection using data mining models. J Next Gener Inf Technol., 2013, 4, 96-107.
[26]
Rajeswari, P.; Reena, G.S. Analysis of liver disorder using data mining algorithm. Glob J Comput Sci Technol., 2012, 10(14), 49.
[27]
Kousarrizi, M.R.N.; Seiti, F.; Teshnehlab, M. An experimental comparative study on thyroid disease diagnosis based on feature subset selection and classification. Int J Electr Comput Sci., 2012, 12(1), 13-19.
[28]
Chaurasia, V.; Pal, S. Data mining approach to detect heart disease. Int J Adv Comput Sci Inf Technol., 2013, 2(4), 56-66.
[29]
Hussain, S.; Hussain, M.; Afzal, M.; Hussain, J.; Bang, J.; Seung, H.; Lee, S. Semantic preservation of standardized healthcare documents in big data. Int. J. Med. Inform., 2019, 129, 133-145.
[http://dx.doi.org/10.1016/j.ijmedinf.2019.05.024] [PMID: 31445248]
[30]
Dhanashree, S.; Mayur, P.; Shruti, D. Heart disease prediction system using naive Bayes. Int. J. Enhanc. Res. Sci. Technol. Eng., 2013, 2(3), 290-294.
[31]
Sarwar, A.; Sharma, V. Intelligent naïve Bayes approach to diagnose diabetes type-2. Int J Comput Appl Chall Netw Intell Comput Technol., 2012, 3(14–16)
[32]
Shajahaan, S.S.; Shanthi, S.; Manochitra, V. Application of data mining techniques to model breast cancer data. Int. J. Emerg. Technol. Adv. Eng., 2013, 3(11), 362-369.
[33]
Sivakami, K.; Application, C. Mining big data: Breast cancer prediction using DT-SVM hybrid model. Int J Sci Eng Appl Sci., 2015, 1(5), 418-429.
[34]
Venkatesan, E.; Velmurugan, T. Performance analysis of decision tree algorithms for breast cancer classification. Indian J. Sci. Technol., 2015, 8(29), 1-8.
[http://dx.doi.org/10.17485/ijst/2015/v8i1/84646]
[35]
Shrivastava, S.S.; Sant, A.; Aharwal, R.P. An overview on data mining approach on breast cancer data. Int J Adv Comput Res., 2013, 3, 256-262.
[36]
Chen, Z.; Smith, D.B. Heterogeneous machine-type communications in cellular networks: Random access optimization by deep reinforcement learning. IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 24 May 2018, pp. 1-6.
[http://dx.doi.org/10.1109/ICC.2018.8422775.]
[37]
Sa, S.; Rai, B.K.; Meshram, A.A.; Gunasekaran, A.; Chandrakumarmangalam, S. Big data in healthcare management: A review of literature. American Journal of Theoretical and Applied Business, 2018, 4(2), 57-69.
[http://dx.doi.org/10.11648/j.ajtab.20180402.14]
[38]
Liu, Y.; Logan, B.; Liu, N.; Xu, Z.; Tang, J.; Wang, Y. Deep reinforcement learning for dynamic treatment regimes on medical registry data. IEEE International Conference on Healthcare Informatics, Aug 2017, pp. 380-385
[http://dx.doi.org/10.1109/ICHI.2017.45]
[39]
Shi, D.; Zurada, J.M.; Guan, Y. A neuro-fuzzy system with semi-supervised learning for bad debt recovery in the healthcare industry; Hawaii Int Conf Syst Sci, 2015, pp. 3115-3124.
[http://dx.doi.org/10.1109/HICSS.2015.376]
[40]
Bandyopadhyay, S.; Karmakar, C.; Banerjee, I. An unsupervised learning for robust cardiac feature derivation from PPG signals. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),, Orlando, FL, USA, 20 August 2016, pp. 740-743.
[http://dx.doi.org/10.1109/EMBC.2016.7590808]
[41]
Wojtusiak, J. Semantic data types in machine learning from healthcare data. Int Conf Mach Learn Appl., 2012, 1, 197-202.
[http://dx.doi.org/10.1109/ICMLA.2012.41]
[42]
Davenport, T.H.; Harris, J.G. Competing on analytics, the new science of winning; Harvard Business School Publishing Corporation: Boston, MA, 2007.
[43]
Chen, H.; Chiang, R.H.; Storey, V.C. Business intelligence and analytics: From big data to big impact. Manage. Inf. Syst. Q., 2012, 36(4), 1165-1188.
[http://dx.doi.org/10.2307/41703503]
[44]
Thuemmler, C. The case for health 4.0. In: Health 4.0: How virtualization and big data are revolutionizing healthcare; Thuemmler, C.; Bai, C., Eds.; Springer: New York, 2017.
[http://dx.doi.org/10.1007/978-3-319-47617-9_1]
[45]
Corsi, A.; de Souza, F.F.; Pagani, R.N.; Kovaleski, J.L. Big data analytics as a tool for fighting pandemics: A systematic review of literature. J. Ambient Intell. Humaniz. Comput., 2021, 12(10), 9163-9180.
[http://dx.doi.org/10.1007/s12652-020-02617-4] [PMID: 33144892]
[46]
Gupta, V.; Rathmore, N. Deriving business intelligence from unstructured data. Int J Inf Comput Technol., 2013, 3(9), 971-976.
[47]
Jain, N.; Gupta, V.; Shubham, S. Understanding cartoon emotion using integrated deep neural network on large dataset. Neural Comput. Appl., 2021.
[PMID: 33903785]
[48]
Williams, N; Ferdinand, NP; Croft, R Project management maturity in the age of big data. Int. J. Manag. Proj. Bus., 2014, 7(2), 311-317.
[49]
Williams, N.; Ferdinand, N.P.; Croft, R. Project management maturity in the age of big data. Int. J. Managing Proj. Bus., 2014, 7(2), 311-317.
[http://dx.doi.org/10.1108/IJMPB-01-2014-0001]
[50]
Shubham, S.; Jain, N.; Gupta, V. Identify glomeruli in human kidney tissue images using a deep learning approach. Soft Comput, 2023, 27, 2705-2716.
[http://dx.doi.org/10.1007/s00500-021-06143-z]
[51]
Provost, F.; Fawcett, T. Data science and its relationship to big data and data-driven decision making. Big Data, 2013, 1(1), 51-59.
[http://dx.doi.org/10.1089/big.2013.1508] [PMID: 27447038]
[52]
Wu, J.; Wang, J.; Nicholas, S.; Maitland, E.; Fan, Q. Application of big data technology for COVID-19 prevention and control in China: Lessons and recommendations. J. Med. Internet Res., 2020, 22(10), e21980.
[http://dx.doi.org/10.2196/21980] [PMID: 33001836]
[53]
Olszak, C.M. Toward better understanding and use of business intelligence in organizations. Inf. Syst. Manage., 2016, 33(2), 105-123.
[http://dx.doi.org/10.1080/10580530.2016.1155946]
[54]
Olszak, C.M.; Mach-Król, M. A conceptual framework for assessing an organization’s readiness to adopt big data. Sustainability, 2018, 10(10), 3734.
[http://dx.doi.org/10.3390/su10103734]
[55]
Palanisamy, V.; Thirunavukarasu, R. Implications of big data analytics in developing healthcare frameworks – A review. J. King Saud Univ. Comput. Inf. Sci., 2019, 31(4), 415-425.
[http://dx.doi.org/10.1016/j.jksuci.2017.12.007]
[56]
Bainbridge, M. Big data challenges for clinical and precision medicine. In: Big Data, Big Challenges: A Healthcare Perspective: Background, Issues, Solutions and Research Directions; Househ, M.; Kushniruk, A.; Borycki, E., Eds.; Springer: Cham, Switzerland, 2019; pp. 17-31.
[http://dx.doi.org/10.1007/978-3-030-06109-8_2]
[57]
Ohlhorst, F. Big data analytics: Turning big data into big money; Wiley: Hoboken, 2012.
[http://dx.doi.org/10.1002/9781119205005]
[58]
Ratia, M.; Myllärniemi, J. Beyond IC 4.0: The future potential of BI-tool utilization in the private healthcare. Proc IFKAD, 2018, 1-13.
[59]
Bahar, N.A.H.A.; Al-Ouqaili, M.T.S.; Talib, N.M. Improving the Diagnosis and Follow‐Up of Chronic Myeloid Leukemia Using Conventional and Molecular Techniques. J. Clin. Lab. Anal., 2025, 39(5), e70001.
[http://dx.doi.org/10.1002/jcla.70001] [PMID: 39927600]
[60]
Jordan, S.R. Beneficence and the expert bureaucracy. Public Integr., 2014, 16(4), 375-394.
[http://dx.doi.org/10.2753/PIN1099-9922160404]
[61]
Davenport, T.H. Big data at work: Dispelling the myths, uncovering the opportunities; Harvard Business School Publishing: Boston, MA, 2014.
[http://dx.doi.org/10.15358/9783800648153]
[62]
Groves, P; Kayyali, B; Knott, D; Van Kuiken, S. The ‘big data’ revolution in healthcare: Accelerating value and innovation. McKinsey Company, 2015.
[63]
Philip Chen, C.L.; Zhang, C.Y. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Inf. Sci., 2014, 275, 314-347.
[http://dx.doi.org/10.1016/j.ins.2014.01.015]
[64]
Tsai, C.W.; Lai, C.F.; Chao, H.C. Big data analytics: A survey. J. Big Data, 2015, 2(21)
[PMID: 26191487]
[65]
De Cnudde, S.; Martens, D. Loyal to your city? A data mining analysis of a public service loyalty program. Decis. Support Syst., 2015, 73, 74-84.
[http://dx.doi.org/10.1016/j.dss.2015.03.004]
[66]
Gandomi, A.; Haider, M. Beyond the hype: Big data concepts, methods, and analytics. Int. J. Inf. Manage., 2015, 35(2), 137-144.
[http://dx.doi.org/10.1016/j.ijinfomgt.2014.10.007]
[67]
Wang, Y.; Byrd, T.A. Business analytics-enabled decision-making effectiveness through knowledge absorptive capacity in health care. J. Knowl. Manage., 2017, 21(3), 517-539.
[http://dx.doi.org/10.1108/JKM-08-2015-0301]
[68]
Janssen, M.; van der Voort, H.; Wahyudi, A. Factors influencing big data decision-making quality. J. Bus. Res., 2017, 70, 338-345.
[http://dx.doi.org/10.1016/j.jbusres.2016.08.007]
[69]
Harerimana, G.; Jang, B.; Kim, J.W.; Park, H.K. Health big data analytics: A technology survey. IEEE Access, 2018, 6, 65661-65678.
[http://dx.doi.org/10.1109/ACCESS.2018.2878254]
[70]
Ismail, A.; Shehab, A.; El-Henawy, I.M. Security in smart cities: Models, applications, and challenges. In: In: Healthcare analysis in smart big data analytics: Reviews, challenges and recommendations; Springer: Cham, Switzerland, 2019; pp. 27-45.
[71]
Al Mayahi, S.; Al-Badi, A.; Tarhini, A. Exploring the potential benefits of big data analytics in providing smart healthcare. In: In: Emerging Technologies in Computing; Springer: Cham, 2018; pp. 247-258.
[http://dx.doi.org/10.1007/978-3-319-95450-9_21]
[72]
Willems, S.M.; Abeln, S.; Feenstra, K.A.; de Bree, R.; van der Poel, E.F.; Baatenburg de Jong, R.J.; Heringa, J.; van den Brekel, M.W.M. The potential use of big data in oncology. Oral Oncol., 2019, 98, 8-12.
[http://dx.doi.org/10.1016/j.oraloncology.2019.09.003] [PMID: 31521885]
[73]
Chomiak-Orsa, I.; Mrozek, B. Główne perspektywy wykorzystania big data w mediach społecznościowych. Informatyka Ekonomiczna., 2017, 3(45), 44-54.
[http://dx.doi.org/10.15611/ie.2017.3.04]
[74]
Mohammadi, M.; Al-Fuqaha, A.; Sorour, S.; Guizani, M. Deep learning for IoT big data and streaming analytics: A survey. IEEE Commun. Surv. Tutor., 2018, 20(4), 2923-2960.
[http://dx.doi.org/10.1109/COMST.2018.2844341]
[75]
Saleh, R.O.; Al-Ouqaili, M.T.S.; Ali, E.; Alhajlah, S.; Kareem, A.H.; Shakir, M.N.; Alasheqi, M.Q.; Mustafa, Y.F.; Alawadi, A.; Alsaalamy, A. lncRNA-microRNA axis in cancer drug resistance: Particular focus on signaling pathways. Med. Oncol., 2024, 41(2), 52.
[http://dx.doi.org/10.1007/s12032-023-02263-8] [PMID: 38195957]
[76]
Islam, M.S.; Hasan, M.M.; Wang, X.; Germack, H. A systematic review on healthcare analytics: Application and theoretical perspective of data mining. Healthcare; Multidisciplinary Digital Publishing Institute: Basel, Switzerland, 2018, p. 54.
[77]
Nambiar, R.; Bhardwaj, R.; Sethi, A.; Vargheese, R. A look at challenges and opportunities of big data analytics in healthcare. 2013 IEEE International Conference on Big Data, Silicon Valley, CA, USA, 09 October 2013, pp. 17-22.
[http://dx.doi.org/10.1109/BigData.2013.6691753]
[78]
Gupta, V.; Singh, V.K.; Ghose, U.; Mukhija, P. A quantitative and text-based characterization of big data research. J. Intell. Fuzzy Syst., 2019, 36(5), 4659-4675.
[http://dx.doi.org/10.3233/JIFS-179016]
[79]
Mehta, N.; Pandit, A. Concurrence of big data analytics and healthcare: A systematic review. Int. J. Med. Inform., 2018, 114, 57-65.
[http://dx.doi.org/10.1016/j.ijmedinf.2018.03.013] [PMID: 29673604]
[80]
Michael, M.; Lupton, D. Toward a manifesto for the ‘public understanding of big data’. Public Underst. Sci., 2016, 25(1), 104-116.
[http://dx.doi.org/10.1177/0963662515609005] [PMID: 26468128]
[81]
Marconi, K.; Dobra, M.; Thompson, C. The use of big data in healthcare. In: Big data and business analytics; Liebowitz, J., Ed.; CRC Press: Boca Raton, 2012; pp. 229-248.
[82]
Manyika, J.; Chui, M.; Brown, B.; Bughin, J.; Dobbs, R.; Roxburgh, C. Big data: The next frontier for innovation, competition, and productivity; McKinsey Global Institute: Washington, 2011.
[83]
Madsen, L.B. Data-driven healthcare: How analytics and BI are transforming the industry; Wiley: Hoboken, 2014.
[84]
Raguseo, E. Big data technologies: An empirical investigation on their adoption, benefits and risks for companies. Int. J. Inf. Manage, 2018, 38(1), 187-195.
[85]
Bartuś, K.; Batko, K.; Lorek, P. Diagnosis of the use of big data in organizations – selected research results Economic Informatics, 2017, 3(45), 9-20.
[86]
Boerma, T.; Requejo, J.; Victora, C.G.; Amouzou, A.; George, A.; Agyepong, I.; Barroso, C.; Barros, A.J.D.; Bhutta, Z.A.; Black, R.E.; Borghi, J.; Buse, K.; Aguirre, L.C.; Chopra, M.; Chou, D.; Chu, Y.; Claeson, M.; Daelmans, B.; Davis, A.; DeJong, J.; Diaz, T.; El Arifeen, S.; Ewerling, F.; Fox, M.; Gillespie, S.; Grove, J.; Guenther, T.; Haakenstad, A.; Hosseinpoor, A.R.; Hounton, S.; Huicho, L.; Jacobs, T.; Jiwani, S.; Keita, Y.; Khosla, R.; Kruk, M.E.; Kuo, T.; Kyobutungi, C.; Langer, A.; Lawn, J.E.; Leslie, H.; Liang, M.; Maliqi, B.; Manu, A.; Masanja, H.; Marchant, T.; Menon, P.; Moran, A.C.; Mujica, O.J.; Nambiar, D.; Ohiri, K.; Park, L.A.; Patton, G.C.; Peterson, S.; Piwoz, E.; Rasanathan, K.; Raj, A.; Ronsmans, C.; Saad-Haddad, G.; Sabin, M.L.; Sanders, D.; Sawyer, S.M.; da Silva, I.C.M.; Singh, N.S.; Somers, K.; Spiegel, P.; Tappis, H.; Temmerman, M.; Vaz, L.M.E.; Ved, R.R.; Vidaletti, L.P.; Waiswa, P.; Wehrmeister, F.C.; Weiss, W.; You, D.; Zaidi, S. Countdown to 2030: Tracking progress towards universal coverage for reproductive, maternal, newborn, and child health. Lancet, 2018, 391(10129), 1538-1548.
[http://dx.doi.org/10.1016/S0140-6736(18)30104-1] [PMID: 29395268]
[87]
Bose, R. Competitive intelligence process and tools for intelligence analysis. Ind. Manage. Data Syst., 2008, 108(4), 510-528.
[http://dx.doi.org/10.1108/02635570810868362]
[88]
Bychkov, D.; Linder, N.; Turkki, R.; Nordling, S.; Kovanen, P.E.; Verrill, C.; Walliander, M.; Lundin, M.; Haglund, C.; Lundin, J. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep., 2018, 8(1), 3395.
[http://dx.doi.org/10.1038/s41598-018-21758-3] [PMID: 29467373]
[89]
Fang, H.; Zhang, Z.; Wang, C.J.; Daneshmand, M.; Wang, C.; Wang, H. A survey of big data research. IEEE Netw., 2015, 29(5), 6-9.
[http://dx.doi.org/10.1109/MNET.2015.7293298] [PMID: 26504265]
[90]
Hampel, H.; O’Bryant, S.E.; Castrillo, J.I.; Ritchie, C.; Rojkova, K.; Broich, K.; Benda, N.; Nisticò, R.; Frank, R.A.; Dubois, B.; Escott-Price, V.; Lista, S. Precision medicine the golden gate for detection, treatment and prevention of Alzheimer’s disease. J. Prev. Alzheimers Dis., 2016, 3(4), 1-17.
[http://dx.doi.org/10.14283/jpad.2016.112] [PMID: 28344933]
[91]
Knapp, M.M. Big Data. J. Electron. Resour. Med. Libr., 2013, 10(4), 215-222.
[http://dx.doi.org/10.1080/15424065.2013.847713]
[92]
Lerner, I.; Veil, R.; Nguyen, D.P.; Luu, V.P.; Jantzen, R. Revolution in healthcare: How will data science impact doctor-patient relationships? Front. Public Health, 2018, 6, 99.
[http://dx.doi.org/10.3389/fpubh.2018.00099] [PMID: 29666789]
[93]
Ma, K.; Wang, X.; Lerner, A.; Shiroishi, M.; Amezcua, L.; Liu, B. Big data in multiple sclerosis: Development of a web-based longitudinal study viewer in an imaging informatics-based eFolder system for complex data analysis and management. Proceedings of the SPIE, Bellingham (WA), March 2015, pp. 941809.
[http://dx.doi.org/10.1117/12.2082650]
[94]
Mikalef, P.; Krogstie, J. Big data analytics as an enabler of process innovation capabilities: A configurational approach. In: In: International Conference on Business Process Management; Springer: Cham, 2018; pp. 426-41.
[http://dx.doi.org/10.1007/978-3-319-98648-7_25]
[95]
Rumsfeld, J.S.; Joynt, K.E.; Maddox, T.M. Big data analytics to improve cardiovascular care: Promise and challenges. Nat. Rev. Cardiol., 2016, 13(6), 350-359.
[http://dx.doi.org/10.1038/nrcardio.2016.42] [PMID: 27009423]
[96]
Vijayarani, S. Liver disease prediction using SVM and naïve Bayes algorithms. Int J Sci Eng Technol Res., 2015, 4(4), 816-820.
[97]
Applying big data analytics in bioinformatics and medicine; Lytras, M.D.; Papadopoulou, P., Eds.; IGI Global: Hershey, 2017.
[98]
Kruse, C.S.; Goswamy, R.; Raval, Y.; Marawi, S. Challenges and opportunities of big data in healthcare: A systematic review. JMIR Med. Inform., 2016, 4(4), e38.
[http://dx.doi.org/10.2196/medinform.5359] [PMID: 27872036]
[99]
Jee, K.; Kim, G.H. Potentiality of big data in the medical sector: Focus on how to reshape the healthcare system. Healthc. Inform. Res., 2013, 19(2), 79-85.
[http://dx.doi.org/10.4258/hir.2013.19.2.79] [PMID: 23882412]
[100]
Krumholz, H.M. Big data and new knowledge in medicine: The thinking, training, and tools needed for a learning health system. Health Aff. (Millwood), 2014, 33(7), 1163-1170.
[http://dx.doi.org/10.1377/hlthaff.2014.0053] [PMID: 25006142]
[101]
Koti, M.S.; Alamma, B.H. Predictive analytics techniques using big data for healthcare databases. In: In: Smart intelligent computing and applications; Springer: New York, 2019; pp. 679-686.
[http://dx.doi.org/10.1007/978-981-13-1927-3_71]
[102]
Abouelmehdi, K.; Beni-Hessane, A.; Khaloufi, H. Big healthcare data: Preserving security and privacy. J. Big Data, 2018, 5(1), 1.
[http://dx.doi.org/10.1186/s40537-017-0110-7]
[103]
Agrawal, Ankit; Choudhary, Alok Health services data: Big data analytics for deriving predictive healthcare insights. In: In: Health Services Evaluation; , 2019; pp. 3-18.
[http://dx.doi.org/10.1007/978-1-4939-8715-3_2]
[104]
Schulte, T; Bohnet-Joschko, S How can big data analytics support people-centred and integrated health services: A scoping review Int. J. Integ. Care, 2022 Jun 16;22(2), 23.
[105]
Batko, K.; Ślęzak, A. The use of Big Data Analytics in healthcare. J. Big Data, 2022, 9(1), 3.
[http://dx.doi.org/10.1186/s40537-021-00553-4] [PMID: 35013701]
[106]
Batko, K Possibilities of using big data in healthcare Annals of the College of Economic Analysis, 2016, 42, 267-282.
[107]
Bi, Z.; Cochran, D. Big data analytics with applications. J Manag Anal., 2014, 1(4), 249-265.
[108]
Bollier, D.; Firestone, C.M. The promise and peril of big data; Aspen Institute, Communications and Society Program: Washington, D.C., 2010.
[109]
Carter, P. Big data analytics: Future architectures, skills and roadmaps for the CIO. In: White paper; IDC, sponsored by SAS, 2011.
[110]
Erickson, S.; Rothberg, H. Data, information, and intelligence. In: The analytics process; Rodriguez, E., Ed.; Auerbach citations: Boca Raton, 2017; pp. 111-126.
[http://dx.doi.org/10.1201/9781315161501-4]
[111]
Fredriksson, C. Organizational knowledge creation with big data: A case study of the concept and practical use of big data in a local government context. 2016.
[112]
Hu, H.; Wen, Y.; Chua, T.S.; Li, X. Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2014, 2, 652-687.
[http://dx.doi.org/10.1109/ACCESS.2014.2332453]

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