Skip to main content
SpringerLink
Log in

Recommendation Systems in Healthcare

  • Chapter
  • First Online:
Recommender Systems for Medicine and Music

Part of the book series: Studies in Computational Intelligence ((SCI,volume 946))

Abstract

Recommender systems are a subclass of information filtering systems which is broadly used to find the right products or services for the right consumers. In the era of ‘Big Data’, recommender systems are of great importance as they are used to solve the information overload in many areas of people’s lives (e-commerce/e-shopping, e-library, movies, TV programs, prescriptions, health recommendations, news, cooking, and more). In their simplest form, recommender systems create a personalized list of predictions that are ranked based on the user’s history, preference, solution/domain constraints, and other factors.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
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

Similar content being viewed by others

References

  1. Ali, R., Afzal, M., et al.: Multimodal hybrid reasoning methodology for personalized wellbeing services. Comput. Biol. Med. 69, 10–28 (2016). Elsevier

    Google Scholar 

  2. Al-Mardini, M., Raś, Z.M.: A supervised model for predicting the risk of mortality and hospital readmissions for newly admitted patients. In: Foundations of Intelligent Systems, Proceedings of ISMIS’17 in Warsaw, Poland, LNAI, vol. 10352, pp. 29–36. Springer (2017)

    Google Scholar 

  3. Al-Mardini, M., Hajja, A., Raś, Z.W.: Reduction of readmissions to hospitals based on actionable knowledge discovery and personalization. In: Beyond Databases Architectures and Structures-BDAS, et al.: Conference Proceedings, Communications in Computer and Information Science, vol. 613, pp. 39–55. Springer (2016)

    Google Scholar 

  4. Al-Mardini, M., Hajja, A., Raś, Z.W. et. al.: Predicting the primary medical procedure through clustering of patients’ diagnoses. In: New Frontiers in Mining Complex Patterns, LNAI, vol. 10312, pp. 117–131. Springer (2017)

    Google Scholar 

  5. Arbajian, P., Hajja, A., Raś, Z.W., Wieczorkowska, A.: Effect of speech segment samples selection in stutter block detection and remediation. J. Intell. Inform. Syst. 53(2), 241–264 (2019). Springer

    Google Scholar 

  6. Arbajian, P., Hajja, A., Raś, Z.W., Wieczorkowska, A.: Segment-removal based stuttered speech remediation. In New Frontiers in Mining Complex Patterns, Post-Proceedings of NFMCP 2017, ECML/PKDD Workshop in Skopje, Macedonia, LNAI, vol. 10785, pp. 16-34. Springer (2018)

    Google Scholar 

  7. Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama 318(22), 2199–2210 (2017)

    Article  Google Scholar 

  8. Bhattacharya, B., Burhanuddin, I., Sancheti, A., Satya, K.: Intent-Aware Contextual Recommendation System, IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1–8. LA, New Orleans (2017)

    Google Scholar 

  9. Bobadilla, J., et al.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)

    Article  Google Scholar 

  10. Branden, M., Wiratunga, N., Burton, D., Craw, S.: Integrating case-based reasoning with an electronic patient record system. Artif. Intell. Med. 51(2), 117–123 (2011)

    Article  Google Scholar 

  11. Ciecierski, K., Raś, Z.W., Przybyszewski, A.: Selection of the optimal electrode in Parkinson’s disease DBS treatment, Foundations of Intelligent Systems, Proceedings of ISMIS 2011 Symposium, LNAI, vol. 6804, pp. 554-564. Springer (2011)

    Google Scholar 

  12. Ciecierski, K., Raś, Z.W., Przybyszewski, A.W.: Intraoperative Decision Making with Rough Set Rules for STN DBS in Parkinson Disease, Proceedings of the International Conference on Brain Informatics and Health, in Warsaw, Poland, LNCS, vol. 8609, pp. 323–334. Springer, (2014)

    Google Scholar 

  13. Ciecierski, K., Raś, Z.W., Przybyszewski, A.: Foundations of automatic system for intrasurgical localization of Subthalamic Nucleus in Parkinson patients. Web Intell. Agent Syst. Int. J. 12(1), 63–82 (2014). IOS Press

    Google Scholar 

  14. Ciecierski, K., Raś, Z.W., Przybyszewski, A.: Foundations of recommender system for STN localization during DBS surgery in Parkinson’s patients. Foundations of Intelligent Systems, Proceedings of ISMIS 2012 Symposium, LNAI, vol. 7661, pp. 234–243. Springer (2012)

    Google Scholar 

  15. Dara, S., Chowdary, C.R., Kumar, C.: A survey on group recommender systems. J. Intell. Inf. Syst. 54, 271–295 (2020). Springer

    Google Scholar 

  16. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)

    Article  Google Scholar 

  17. Felfernig, A. et al.: An overview of recommender systems in the internet of things. J. Intell. Inf. Syst. 52(2), 285–309 (2019). Springer

    Google Scholar 

  18. Grasser, F., et al.: Neighborhood-based Collaborative Filtering for Therapy Decision Support, Proceedings of the Second International Workshop on Health Recommender Systems, ACM RecSys 2017. Como, Italy (2017)

    Google Scholar 

  19. Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama 316(22), 2402–2410 (2016)

    Article  Google Scholar 

  20. Guo, L., et al.: Collaborative filtering recommendation based on trust and emotion. J. Intell. Inf. Syst. 53, 113–135 (2019). Springer

    Google Scholar 

  21. Hajja, A., Touati, H., Raś, Z.W., Studnicki, J., Wieczorkowska, A.: Predicting negative side effects of surgeries through clustering. In: New Frontiers in Mining Complex Patterns, Post-proceedings of NFMCP, : ECML/PKDD Workshop, Nancy, France, LNAI, vol. 8983, pp. 41–55. Springer (2014)

    Google Scholar 

  22. Hajja, A., Wieczorkowska, A., Raś, Z.W., Gubrynowicz, R.: Pair-based object-driven action rules. In: New Frontiers in Mining Complex Patterns, NFMCP, : ECML/PKDD Workshop, LNAI 7765, pp. 79–93 . Springer (2012)

    Google Scholar 

  23. Kim, T.-H., et al.: Improving prediction quality in collaborative filtering based on clustering. Proceedings of IEEE/WIC/ACM International Conference on WI and IAT, Sydney, NSW, Australia (2008)

    Google Scholar 

  24. Kohli, D., Raś, Z.W. et. al.: From music to emotions and tinnitus treatment, initial study. Foundations of Intelligent Systems, Proceedings of ISMIS 2012 Symposium, LNAI, vol. 7661, pp. 244–253. Springer (2012)

    Google Scholar 

  25. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  26. Lee, J., Kim, S., Lebanon, G., Singer, Y.: Local low-rank matrix approximation. In: Proceedings of 30th International Conference on Machine Learning (ICML ’13), pp. 82–90 (2013)

    Google Scholar 

  27. Lim, T.-P., Husain, W., Zakaria, N.: Recommender system for personalized wellness therapy. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 4(9) (2013). http://dx.doi.org/10.14569/IJACSA.2013.040909

  28. Lopez-Nores, M. et. al.: Enhancing recommender systems with access to electronic health records and groups of interest in social networks. In: 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems, Dijon, pp. 105–110 (2011)

    Google Scholar 

  29. Mardini, M.T., Raś, Z.W.: Extraction of actionable knowledge to reduce hospital readmissions through patients personalization. Inf. Sci. J. 485, 1–17 (2019). Elsevier

    Google Scholar 

  30. Nobahari, V., Jalali, M.: ISoTrustSeq: a social recommender system based on implicit interest, trust and sequential behaviors of users using matrix factorization. J. Intell. Inf. Syst. 52, 239–268 (2019). Springer

    Google Scholar 

  31. Nounou, M.N., Nounou, H.N., Mansouri, M.: Model-based and model-free filtering of genomic data. In: Network Modeling Analysis in Health Informatics and Bioinformatics volume 2, pp. 109–121 (2013). Springer

    Google Scholar 

  32. Priyadarshini, R., Barik, R., Dubey, H., DeepFog: Fog computing-based deep neural architecture for prediction of stress types, diabetes and hypertension attacks. Computation 6(4) (2018)

    Google Scholar 

  33. Pujahari, A., Padmanabhan, V.: Group Recommender systems: combining user-user and item-item collaborative filtering techniques. In: 2015 International Conference on Information Technology (ICIT), Bhubaneswar, pp. 148–152 (2015)

    Google Scholar 

  34. Qayyum, A., et al.: Secure and Robust Machine Learning for Healthcare: A Survey. https://arxiv.org/abs/2001.08103 (2020)

  35. Rahdari, B., Tsai, C., Brusilovsky, P.: Expanding Controllability of Hybrid Recommender Systems: From Positive to Negative Relevance, FLAIRS’19 Proceedings. Fl, Sarasota (2019)

    Google Scholar 

  36. Rajpurkar, P. et al.: Chexnet: Radiologist level pneumonia detection on chest x-rays with deep learning. Cornell University. https://arxiv.org/abs/1711.05225 (2017)

  37. Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In Advances in Neural Information Processing Systems (NIPS ’08), pp. 1257–1264 (2008)

    Google Scholar 

  38. Sanchez Bocanegra, C.L., Sevillano Ramos, J.L., Rizo, C. et al.: Health RecSys: a semantic content-based recommender system to complement health videos. BMC Med. Inf. Decis. Mak 17(63). https://doi.org/10.1186/s12911-017-0431-7 (2017)

  39. Schmidt, R., Gierl, L.: Case-based reasoning for antibiotics therapy advice: an investigation of retrieval algorithms and prototypes. Artif. Intell. Med. 23(2), 171–186 (2001)

    Article  Google Scholar 

  40. Stratigi, M., Kondylakis, H., Stefanidis, K.: Fairness in group recommendations in the health domain. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), Proceedings, pp. 1481–1488 (2017)

    Google Scholar 

  41. Strub, F., et. al.: Hybrid Collaborative Filtering with Neural Networks. https://hal.archives-ouvertes.fr/hal-01281794v1/document (2016)

  42. Tarnowska, K.A., Raś, Z.W., Jastreboff, P.J.: Decision Support System for Diagnosis and Treatment of Hearing Disorders, The Case of Tinnitus, Studies in Computational Intelligence, vol. 685 (2017). Springer

    Google Scholar 

  43. Tarnowska, K.A., Raś, Z.W., Jastreboff, P.J.: Mining for actionable knowledge in tinnitus datasets. In: Thriving Rough Sets, Studies in Computational Intelligence, vol. 708, Chapter 18, pp. 367–396. Springer (2017)

    Google Scholar 

  44. Thompson, P., Zhang, X., Jiang, W., Raś, Z.W.: From mining tinnitus database to tinnitus decision-support system, initial study. In: Proceedings of IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2007), IEEE Computer Society, San Jose, Calif., 203–206 (2007)

    Google Scholar 

  45. Usman, M., et al.: Retrospective motion correction in multi shot MRI using generative adversarial network. Sci. Rep. 10(1), 1–11 (2020)

    Article  Google Scholar 

  46. Waqar, M., Majeed, N. et. al.: An adaptive doctor-recommender system, Behaviour & Information Technology, vol. 38, Taylor & Francis, pp. 959-973 (2019)

    Google Scholar 

  47. Wu, M.Y., et al.: A deep architecture for depression detection using posting, behavior, and living environment data. J. Intell. Inf. Syst. 54, 225–244 (2020)

    Article  Google Scholar 

  48. Yuan, W., et al.: Socialized healthcare service recommendation using deep learning. Neural Comput. Appl. 30, 2071–2082 (2018)

    Article  Google Scholar 

  49. Zhang, Y., et al.: iDoctor: personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Gener. Comput. Syst. 66, 30–35 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of North Carolina, Charlotte, N.C., 28223, USA

    Madlen Ivanova

  2. Department of Computer Science, University of North Carolina, Charlotte, N.C., 28223, USA

    Zbigniew W. Raś

  3. Polish-Japanese Academy of Information Technology, 02-008, Warsaw, Poland

    Zbigniew W. Raś

Authors
  1. Madlen Ivanova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zbigniew W. Raś
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Madlen Ivanova .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, USA

    Zbigniew W. Ras

  2. Department of Multimedia, Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Alicja Wieczorkowska

  3. Department of Medical Informatics, Faculty of Medicine, Shimane University, Izumo, Japan

    Shusaku Tsumoto

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ivanova, M., Raś, Z.W. (2021). Recommendation Systems in Healthcare. In: Ras, Z.W., Wieczorkowska, A., Tsumoto, S. (eds) Recommender Systems for Medicine and Music. Studies in Computational Intelligence, vol 946. Springer, Cham. https://doi.org/10.1007/978-3-030-66450-3_1

Download citation

Publish with us

Policies and ethics

Search

Navigation