Skip to main content
SpringerLink
Log in

Decision-Making in Healthcare Nanoinformatics

  • Chapter
  • First Online:
Next Generation Healthcare Informatics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1039))

  • 324 Accesses

Abstract

Nanotechnology has become one of the most sought after area of research currently. Increase in research in a new research field leads to mostly unorganized, heterogeneous, and huge volume of data. To take benefit from that huge amount of data, the necessity of informatics is paramount. To build nanoinformatics repositories, role of different decision-making methods comes to fore. Among several associated interdisciplinary field of research, healthcare sector is the most important one. So, nanoinformatics is the one of the important areas which need to be addressed quickly to aid future research. This paper provides many insights related to nanotechnology in health care, nanoinformatics, and decision-making methodologies involving in it.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Singh, L., Kruger, H. G., Maguire, G. E., Govender, T., & Parboosing, R. (2017). The role of nanotechnology in the treatment of viral infections. Therapeutic Advances in Infectious Disease, 4(4), 105–131.

    Article  Google Scholar 

  2. Rai, M., Bonde, S., Yadav, A., Bhowmik, A., Rathod, S., Ingle, P., & Gade, A. (2021). Nanotechnology as a shield against COVID-19: Current advancement and limitations. Viruses, 13(7), 1224.

    Article  Google Scholar 

  3. Altman, R. B. (1998). Bioinformatics in support of molecular medicine. In Proceedings of the AMIA Symposium (pp. 53–61). American Medical Informatics Association.

    Google Scholar 

  4. Kulikowski, C. A. (2002). The micro-macro spectrum of medical informatics challenges: From molecular medicine to transforming health care in a globalizing society. Methods of information in medicine, 41(1), 20–24.

    Article  Google Scholar 

  5. Kohane, I. S. (2000). Bioinformatics and clinical informatics: The imperative to collaborate. Journal of the American Medical Informatics Association, 7(5), 512–516.

    Article  Google Scholar 

  6. Maojo, V., & Kulikowski, C. A. (2003). Bioinformatics and medical informatics: Collaborations on the road to genomic medicine? Journal of the American Medical Informatics Association, 10(6), 515–522.

    Article  Google Scholar 

  7. Sooraj, T. R., Mohanty, R. K., & Tripathy, B. K. (2016). Fuzzy soft set theory and its application in group decision making. In Advanced computing and communication technologies (Vol. 452, pp. 171–178). Springer.

    Google Scholar 

  8. Tripathy, B. K., Mohanty, R. K., & Sooraj, T. R. (2016). On intuitionistic fuzzy soft sets and their application in decision-making. In Proceedings of the International Conference on Signal, Networks (ICSNCS-2016), Computing, and Systems Vol. 396, pp. 67–73). Springer.

    Google Scholar 

  9. Tripathy, B. K., Mohanty, R. K., Sooraj, T. R., & Tripathy, A. (2016). A modified representation of IFSS and its usage in GDM. In Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1, Smart Innovation, Systems and Technologies (Vol. 50, pp. 365–375). Springer.

    Google Scholar 

  10. Mohanty, R. K., Sooraj, T. R., & Tripathy, B. K. (2017). IVIFS and decision-making. In Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in intelligent systems and computing (Vol. 468, pp. 319–330). Springer.

    Google Scholar 

  11. Maojo, V., Iakovidis, I., Martin-Sanchez, F., Crespo, J., & Kulikowski, C. (2001). Medical informatics and bioinformatics: European efforts to facilitate synergy. Journal of Biomedical Informatics, 34(6), 423–427.

    Article  Google Scholar 

  12. Martin-Sanchez, F., Iakovidis, I., Nørager, S., Maojo, V., de Groen, P., Van der Lei, J., Jones, T., Abraham-Fuchs, K., Apweiler, R., Babic, A., & Vicente, F. J. (2004). Synergy between medical informatics and bioinformatics: Facilitating genomic medicine for future health care. Journal of Biomedical Informatics, 37(1), 30–42.

    Google Scholar 

  13. Kim, B. Y., Rutka, J. T., & Chan, W. C. (2010). Nanomedicine. New England Journal of Medicine, 363(25), 2434–2443.

    Article  Google Scholar 

  14. Doms, A., & Schroeder, M. (2005). GoPubMed: Exploring PubMed with the gene ontology. Nucleic Acids Research, 33(Suppl 2), W783–W786.

    Google Scholar 

  15. Anandaram, H., & Rashmi, A. B. (2020). A review on application of nanoinformatics and bioinformatics in nanomedicine. Tissue Engineering & Regenerative Medicine Open Access, 6(3), 53–56. https://doi.org/10.15406/atroa.2020.06.00118

    Article  Google Scholar 

  16. Maojo, V., García-Remesal, M., de la Iglesia, D., Crespo, J., Pérez-Rey, D., Chiesa, S., Fritts, M., & Kulikowski, C. A. (2011). Nanoinformatics: Developing advanced informatics applications for nanomedicine. In A. Prokov, (Ed.), Intracellular delivery (pp. 847–860). Springer.

    Google Scholar 

  17. Maojo, V., Martin-Sanchez, F., Kulikowski, C., Rodriguez-Paton, A., & Fritts, M. (2010). Nanoinformatics and DNA-based computing: Catalyzing nanomedicine. Pediatric Research, 67(5), 481–489.

    Article  Google Scholar 

  18. Maojo, V., & Martín-Sánchez, F. (2011). The ACTION-grid white paper: Linking biomedical informatics, grid computing and nanomedicine.

    Google Scholar 

  19. Freitas, R. A. (1999). Nanomedicine, Volume I: Basic capabilities (Vol. 1, pp. 210–219). Landes Bioscience.

    Google Scholar 

  20. Freitas, Jr., R. A. (2003). Volume IIA: Biocompatibility. Landes Bioscience.

    Google Scholar 

  21. Jain, K. K., & Jain, K. K. (2008). The handbook of nanomedicine (Vol. 404, pp. 161–192). Humana Press.

    Google Scholar 

  22. Thomas, D. G., Pappu, R. V., & Baker, N. A. (2011). NanoParticle Ontology for cancer nanotechnology research. Journal of Biomedical Informatics, 44(1), 59–74.

    Article  Google Scholar 

  23. Rosse, C., & Mejino, J. L., Jr. (2003). A reference ontology for biomedical informatics: The Foundational Model of Anatomy. Journal of Biomedical Informatics, 36(6), 478–500.

    Article  Google Scholar 

  24. Maojo, V., Fritts, M., de la Iglesia, D., Cachau, R. E., Garcia-Remesal, M., Mitchell, J. A., & Kulikowski, C. (2012). Nanoinformatics: A new area of research in nanomedicine. International Journal of Nanomedicine, 7, 3867.

    Article  Google Scholar 

  25. de la Iglesia, D., Harper, S., Hoover, M. D., Klaessig, F., Lippell, P., Maddux, B., Morse, J., Nel, A., Rajan, K., Reznik-Zellen, R., & Tuominen, M. T. (2011). Nanoinformatics 2020 roadmap.

    Google Scholar 

  26. de la Iglesia, D., Maojo, V., Chiesa, S., Martin-Sanchez, F., Kern, J., Potamias, G., Crespo, J., Garcia-Remesal, M., Keuchkerian, S., Kulikowski, C., & Mitchell, J. A. (2011). International efforts in nanoinformatics research applied to nanomedicine. Methods of Information in Medicine, 50(1), 84–95.

    Google Scholar 

  27. Kiberstis, P., & Roberts, L. (2002). It’s not just the genes. Science, 296(5568), 685–685.

    Article  Google Scholar 

  28. Green, E. D., & Guyer, M. S. (2011). Charting a course for genomic medicine from base pairs to bedside. Nature, 470(7333), 204–213.

    Article  Google Scholar 

  29. National Institutes of Health. (2004). US National Library of Medicine.

    Google Scholar 

  30. Gordon, N., & Sagman, U. (2010). Nanomedicine taxonomy. Canadian Institutes of Health Research & Canadian NanoBusiness Alliance, 2003. Google Scholar.

    Google Scholar 

  31. Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W., Ceusters, W., Goldberg, L. J., Eilbeck, K., Ireland, A., Mungall, C. J., Leontis, N., Rocca-Serra, P., Ruttenberg, A., Sansone, S.-A., Scheuermann, R. H., Shah, N., Whetzel, P. L., & Lewis, S. (2007). The OBO foundry: Coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology, 25(11), 1251–1255.

    Google Scholar 

  32. de la Calle, G., Garcia-Remesal, M., Chiesa, S., de la Iglesia, D., & Maojo, V. (2009). BIRI: A new approach for automatically discovering and indexing available public bioinformatics resources from the literature. BMC Bioinformatics, 10(1), 1–14.

    Article  Google Scholar 

  33. Viceconti, M., Clapworthy, G., & Jan, S. V. S. (2008). The virtual physiological human—A European initiative for in silico human modelling. The Journal of Physiological Sciences, 58(7), 441–446. 0810200082.

    Google Scholar 

  34. Anandaram, H. (2020). Role of bioinformatics in nanotechnology: An initiation towards personalized medicine. In Data analytics in medicine: Concepts, methodologies, tools, and applications (pp. 1875–1894). IGI Global.

    Google Scholar 

  35. Gerstein, M., Seringhaus, M., & Fields, S. (2007). Structured digital abstract makes text mining easy. Nature, 447(7141), 142.

    Article  Google Scholar 

  36. Maojo, V., Crespo, J., García-Remesal, M., De la Iglesia, D., Perez-Rey, D., & Kulikowski, C. (2011). Biomedical ontologies: Toward scientific debate. Methods of Information in Medicine, 50(03), 203–216.

    Article  Google Scholar 

  37. Bewick, S., Yang, R., & Zhang, M. (2009). Complex mathematical models of biology at the nanoscale. Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology, 1(6), 650–659.

    Google Scholar 

  38. O'donoghue, S. I., Goodsell, D. S., Frangakis, A. S., Jossinet, F., Laskowski, R. A., Nilges, M., Saibil, H. R., Schafferhans, A., Wade, R. C., Westhof, E., & Olson, A. J. (2010). Visualization of macromolecular structures. Nature Methods, 7(3), S42–S55.

    Google Scholar 

  39. Berman, H., Henrick, K., & Nakamura, H. (2003). Announcing the worldwide protein data bank. Nature Structural & Molecular Biology, 10(12), 980.

    Article  Google Scholar 

  40. Palmer, B. W., & Harmell, A. L. (2016). Assessment of healthcare decision-making capacity. Archives of Clinical Neuropsychology, 31(6), 530–540. https://doi.org/10.1093/arclin/acw051

    Article  Google Scholar 

  41. Bates, M. E., Larkin, S., Keisler, J. M., & Linkov, I. (2015). How decision analysis can further nanoinformatics. Beilstein Journal of Nanotechnology, 6(1), 1594–1600.

    Article  Google Scholar 

  42. Tripathy, B. K., Sooraj, T. R., Mohanty, R. K., & Panigrahi, A. (2018). Group decision making through interval valued intuitionistic fuzzy soft sets. International Journal of Fuzzy System Applications (IJFSA), 7(3), 99–117.

    Article  Google Scholar 

  43. Sooraj, T. R., & Tripathy, B. K. (2018). An interval valued fuzzy soft set based optimization algorithm for high yielding seed selection. International Journal of Fuzzy System Applications (IJFSA), 7(2), 44–61.

    Article  Google Scholar 

  44. Sooraj, T. R., & Tripathy, B. K. (2018). Optimization of seed selection for higher product using interval valued fuzzy soft sets. Songklanakarin Journal of Science & Technology, 40(5), 1125–1135.

    Google Scholar 

  45. Tripathy, B. K., Sooraj, T. R., & Mohanty, R. K. (2016). A new approach to fuzzy soft set theory and its application in decision making. In Computational intelligence in data mining—2. Advances in intelligent systems and computing (Vol. 11, pp. 305–313). Springer.

    Google Scholar 

  46. Tripathy, B. K., Mohanty, R. K., Sooraj, T. R., & Arun, K. R. (2016). A new approach to intuitionistic fuzzy soft sets and its application in decision-making. In Proceedings of the International Congress on Information and Communication Technology. Advances in intelligent systems and computing (Vol. 439, pp. 93–100). Springer.

    Google Scholar 

  47. Sooraj, T. R., & Tripathy, B. K. (2017). Interval valued hesitant fuzzy soft sets and its application in stock market analysis. In S. Dash, K. Vijayakumar, B. Panigrahi, & S. Das (Eds.), Artificial intelligence and evolutionary computations in engineering systems. Advances in intelligent systems and computing (Vol. 517, pp. 755–764). Springer.

    Google Scholar 

  48. Sooraj, T. R., Mohanty, R. K., & Tripathy, B. K. (2017). Hesitant fuzzy soft set theory and its application in decision making. In: S. Dash, K. Vijayakumar, B. Panigrahi, S. Das (Eds.), Artificial intelligence and evolutionary computations in engineering systems. Advances in intelligent systems and computing (Vol. 517, pp. 315–322). Springer.

    Google Scholar 

  49. Sooraj, T. R., Mohanty, R. K., & Tripathy, B. K. (2018). Improved decision making through IFSS. In S. Satapathy, V. Bhateja, & S. Das (Eds.), Smart computing and informatics, smart innovation, systems and technologies (Vol. 77, pp. 213–219). Springer.

    Chapter  Google Scholar 

  50. Sooraj, T. R., Mohanty, R. K., & Tripathy, B. K. (2018). A new approach to interval-valued intuitionistic hesitant fuzzy soft sets and their application in decision making. In Proceedings of SCI 2017, Visakhapatnam, Smart Computing and Informatics (pp. 243–253). Springer.

    Google Scholar 

  51. Tripathy, B. K., Mohanty, R. K., Sooraj, T. R., & Arun, K. R. (2017). Parameter reduction in soft set models and application in decision making. In S. Arun Kumar, X. Z. Gao, & A. Abraham (Eds.), Handbook of research on fuzzy and rough set theory in organizational decision making (Chap. 15, pp. 331–354). IGI Global.

    Google Scholar 

  52. Mohanty, R. K., & Tripathy, B. K. (2021). Recommending turmeric variety for higher production using interval-valued fuzzy soft set model and PSO. International Journal of Swarm Intelligence Research (IJSIR), 12(2), 94–110.

    Article  Google Scholar 

  53. Mohanty, R. K., & Tripathy, B. K. (2017). Intuitionistic hesitant fuzzy soft set and its application in decision making. In S. Dash, K. Vijayakumar, B. Panigrahi, & S. Das (Eds.), Artificial intelligence and evolutionary computations in engineering systems (Vol. 517, pp. 221–233). Springer.

    Chapter  Google Scholar 

  54. Mohanty, R. K., & Tripathy, B. K. (2021). An improved approach to group decision-making using intuitionistic fuzzy soft set. Lecture Notes in networks and systemsIn A. Tripathy, M. Sarkar, J. Sahoo, K. C. Li, & S. Chinara (Eds.), Advances in distributed computing and machine learning (Vol. 127, pp. 283–296). Springer.

    Chapter  Google Scholar 

  55. Nobile, S., & Nobile, L. (2017). Nanotechnology for biomedical applications: Recent advances in neurosciences and bone tissue engineering. Polymer Engineering & Science, 57(7), 644–650.

    Article  Google Scholar 

  56. Wu, H., Wang, M. D., Liang, L., Xing, H., Zhang, C. W., Shen, F., Huang, D.S., & Yang, T. (2021). Nanotechnology for hepatocellular carcinoma: from surveillance, diagnosis to management. Small, 17(6), 2005236.

    Google Scholar 

  57. Rai, M., & Ingle, A. (2012). Role of nanotechnology in agriculture with special reference to management of insect pests. Applied Microbiology and Biotechnology, 94(2), 287–293.

    Article  Google Scholar 

  58. Subramani, K. (2006). Applications of nanotechnology in drug delivery systems for the treatment of cancer and diabetes. International Journal of Nanotechnology, 3(4), 557–580.

    Article  MathSciNet  Google Scholar 

  59. Thrall, J. H. (2004). Nanotechnology and medicine. Radiology, 230(2), 315–318.

    Article  Google Scholar 

  60. Kumar, S., Dilbaghi, N., Saharan, R., & Bhanjana, G. (2012). Nanotechnology as emerging tool for enhancing solubility of poorly water-soluble drugs. Bionanoscience, 2(4), 227–250.

    Article  Google Scholar 

  61. Solanki, A., Kim, J. D., & Lee, K. B. (2008). Nanotechnology for regenerative medicine: nanomaterials for stem cell imaging (pp. 567–578).

    Google Scholar 

  62. Guimarães, A., Martins, A., Pinho, E. D., Faria, S., Reis, R. L., & Neves, N. M. (2010). Solving cell infiltration limitations of electrospun nanofiber meshes for tissue engineering applications. Nanomedicine, 5(4), 539–554.

    Article  Google Scholar 

  63. Tervonen, T., Linkov, I., Figueira, J. R., Steevens, J., Chappell, M., & Merad, M. (2009). Risk-based classification system of nanomaterials. Journal of Nanoparticle Research, 11(4), 757–766.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT, Vellore, Tamil Nadu, 632014, India

    R. K. Mohanty

  2. School of Information Technology and Engineering, VIT, Vellore, Tamil Nadu, 632014, India

    B. K. Tripathy

Authors
  1. R. K. Mohanty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. K. Tripathy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. K. Mohanty .

Editor information

Editors and Affiliations

  1. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    B. K. Tripathy

  2. Department of Mathematics and Computer Science, St. Mary’s University, Halifax, NS, Canada

    Pawan Lingras

  3. Bharti School of Telecommunications Technology and Management, Indian Institute of Technology, New Delhi, India

    Arpan Kumar Kar

  4. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    Chiranji Lal Chowdhary

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mohanty, R.K., Tripathy, B.K. (2022). Decision-Making in Healthcare Nanoinformatics. In: Tripathy, B.K., Lingras, P., Kar, A.K., Chowdhary, C.L. (eds) Next Generation Healthcare Informatics. Studies in Computational Intelligence, vol 1039. Springer, Singapore. https://doi.org/10.1007/978-981-19-2416-3_6

Download citation

Publish with us

Policies and ethics

Search

Navigation